Integrated machine-learning framework to estimate homologous recombination deficiency

ABSTRACT

Methods, systems, and software are provided for determining a homologous recombination pathway status of a cancer in a test subject, e.g., to improve cancer treatment predictions and outcomes. In some embodiments, classifiers using one or more of (i) a heterozygosity status for DNA damage repair genes in a cancerous tissue, (ii) a measure of the loss of heterozygosity across the genome of the cancerous tissue, (iii) a measure of variant alleles detected in a second plurality of DNA damage repair genes in the genome of the cancerous tissue, (iv) a measure of variant alleles detected in the second plurality of DNA damage repair genes in the genome of a non-cancerous tissue, and (v) tumor sample purity are provided.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. patentapplication Ser. No. 16/789,363, filed Feb. 12, 2020, which claimspriority to U.S. Provisional Patent Application No. 62/804,730, filed onFeb. 12, 2019, and U.S. Provisional Patent Application No. 62/946,347,filed on Dec. 10, 2019, the contents of which are hereby incorporated byreference in their entireties for all purposes.

TECHNICAL FIELD

The present disclosure relates generally to use of machine-learningclassifiers trained against DNA sequencing of cancerous tissues topredict homologous recombination deficiency.

BACKGROUND

Precision oncology is the practice of tailoring cancer therapy to theunique genomic, epigenetic, and/or transcriptomic profile of anindividual tumor. This is in contrast to conventional methods fortreating a cancer patient based merely on the type of cancer the patientis afflicted with, e.g., treating all breast cancer patients with afirst therapy and all lung cancer patients with a second therapy.Precision oncology was borne out of many observations that differentpatients diagnosed with the same type of cancer, e.g., breast cancer,responded very differently to common treatment regimes. Over time,researchers have identified genomic, epigenetic, and transcriptomicmarkers that facilitate some level of prediction as to how an individualcancer will respond to a particular treatment modality.

Therapy targeted to specific genomic alterations is already the standardof care in several tumor types (e.g., as suggested in the NationalComprehensive Cancer Network (NCCN) guidelines for melanoma, colorectalcancer, and non-small cell lung cancer). These few, well known mutationsin the NCCN guidelines can be addressed with individual assays or smallnext generation sequencing (NGS) panels. However, for the largest numberof patients to benefit from personalized oncology, molecular alterationsthat can be targeted with off-label drug indications, combinationtherapy, or tissue agnostic immunotherapy should be assessed. SeeSchwaederle et al. 2016 JAMA Oncol. 2, 1452-1459; Schwaederle et al.2015 J Clin Oncol. 32, 3817-3825; and Wheler et al. 2016 Cancer Res. 76,3690-3701. Large panel NGS assays also cast a wider net for clinicaltrial enrollment. See Coyne et al. 2017 Curr. Probl. Cancer 41, 182-193;and Markman 2017 Oncology 31, 158, 168.

Genomic analysis of tumors is rapidly becoming routine clinical practiceto provide tailored patient treatments and improve outcomes. SeeFernandes et al. 2017 Clinics 72, 588-594. Indeed, recent studiesindicate that clinical care is guided by NGS assay results for 30-40% ofpatients receiving such testing. See Hirshfield et al. 2016 Oncologist21, 1315-1325; Groisberg et al. 2017 Oncotarget 8, 39254-39267; Ross etal. JAMA Oncol. 1, 40-49; and Ross et al. 2015 Arch. Pathol. Lab Med.139, 642-649. There is growing evidence that patients who receivetherapeutic advice guided by genetics have better outcomes. See, forexample Wheler et al. who used matching scores (e.g., scores based onthe number of therapeutic associations and genomic aberrations perpatient) to demonstrate that patients with higher matching scores have agreater frequency of stable disease, longer time to treatment failure,and greater overall survival (2016 Cancer Res. 76, 3690-3701). Suchmethods may be particularly useful for patients who have already failedmultiple lines of therapy.

Targeted therapies have shown significant improvements in patientoutcomes, especially in terms of progression-free survival. See Radovichet al. 2016 Oncotarget 7, 56491-56500. Recent evidence reported from theIMPACT trial, which involved genetic testing of advanced stage tumorsfrom 3,743 patients and where approximately 19% of patients receivedmatched targeted therapies based on their tumor biology, showed aresponse rate of 16.2% in patients with matched treatments versus 5.2%in patients with non-matched treatments. See Bankhead. “IMPACT Trial:Support for Targeted Cancer Tx Approaches.” MedPageToday. Jun. 5, 2018.The IMPACT study further found that the three-year overall survival forpatients given a molecularly matched therapy was more than twice that ofnon-matched patients (15% vs. 7%). See Id. and ASCO Post. “2018 ASCO:IMPACT Trial Matches Treatment to Genetic Changes in the Tumor toImprove Survival Across Multiple Cancer conditions.” The ASCO POST. Jun.6, 2018. Estimates of the proportion of patients for whom genetictesting changes the trajectory of their care vary widely, fromapproximately 10% to more than 50%. See Fernandes et al. 2017 Clinics72, 588-594.

One example of a genomic trait that has been linked to the efficacy ofparticular therapies are mutations in the BRCA1, BRCA2, or PALB2homologous recombination genes. A class of pharmacological inhibitors ofPoly ADP ribose polymerase 1 (PARP1), known as PARP inhibitors (PARPi),have therapeutic efficacy for treating some cancers containing amutation in the BRCA1, BRCA2, or PALB2 homologous recombination genes.PARP1 is an essential enzyme in the error-prone microhomology-mediatedend joining (MMEJ) DNA repair pathway. Sharma S. et al., Cell Death Dis.6(3):e1697 (2015). In the absence of PARP1 activity, DNA replicationforks stall when encountering a single-strand break. Fork stallingultimately results in double-stranded chromosomal breaks that can berepaired by homologous recombination (HR) repair, which is much lesserror prone than the MMEJ pathway.

Unlike other DNA repair proteins, which are commonly deficient in cancercells, PARP1 has been shown to be over-expressed in certain cancertypes. It has been theorized that increased MMEJ DNA repair, relative tohomologous repair, results in the accumulation of genomic mutations,which can lead to the development of cancer. However, the efficacy ofPARP inhibitors is not completely understood. For instance, not allcancers with a BRCA1, BRCA2, or PALB2 mutation are sensitive to PARPinhibitors. Further, some cancers without a mutation in any homologousrecombination protein are sensitive to PARP inhibitors.

Homologous recombination (HR) is a normal, highly conserved DNA repairprocess that enables the exchange of genetic information betweenidentical or closely related DNA molecules. It is most widely used bycells to accurately repair harmful breaks (i.e. damage) that occur onboth strands of DNA. DNA damage may occur from exogenous (external)sources like UV light, radiation, or chemical damage; or from endogenous(internal) sources like errors in DNA replication or other cellularprocesses that create DNA damage. Double strand breaks are a type of DNAdamage.

Using poly (ADP-ribose) polymerase (PARP) inhibitors in patients withHRD compromises two pathways of DNA repair, resulting in cell death(apoptosis). The efficacy of PARP inhibitors is improved not only inovarian cancers displaying germline or somatic BRCA mutations, but alsoin cancers in which HRD is caused by other underlying etiologies.

Poly (ADP-ribose) polymerase (PARP) is a family of proteins involved ina number of cellular processes such as DNA repair, genomic stability,and programmed cell death. Homologous recombination deficiency (“HRdeficiency” or “HRD”) is a deficiency that has been shown to increasethe efficacy of PARP inhibitors (PARPi) and platinum-based therapies forpatients. The most common lesions in cell DNA are single strand breaks(SSB), happening in tens of thousands per cells per day. PARPs are DNArepair enzymes that help repair single stranded breaks. When these PARPsare not working or are blocked (through a PARP inhibitor therapy, forexample), this often leads to what are called double stranded breaks(DSBs). Homologous recombination repair (HRR) is the main way the bodyrepairs these DSBs. If cancer cells have HRD (or, in other words,deficient HRR), the likelihood of the cell recovering from the DSBlowers, leading the cell into apoptosis (programmed cell death), insteadof the cell continuing to proliferate. Causing cancer cells to die isone way to stop a person's cancer from growing.

HRD is considered by some as a disease state arising in tumors throughloss of the homologous recombination DNA repair pathway, commonly causedby biallelic inactivation of BRCA1/2. The deficiency is often signaledby a mutation in the BRCA genes, but, as is common in cancer, there areother ways a tumor can have a HR deficiency.

Across cancers, HRD occurs at a frequency of about 6%. Rates can be ashigh as 30% for ovarian cancer, and intermediate for breast, pancreaticand prostate cancer (12-13%). HRD may be driven by biallelicinactivation of BRCA1, BRCA2, RAD51C and PALB2. Loss of heterozygosity(LOH) and deletions (especially of BRCA2) are also thought to be a majorcause.

SUMMARY

Given the above background, what is needed in the art are improved waysto predict which cancers are homologous repair deficient (HRD), e.g., toidentify which cancer patients are more likely to respond favorably toPARP inhibitors. The present disclosure addresses these and other needsby providing systems and methods for evaluating DNA sequencing resultsfrom cancerous tissues using a machine-learning algorithm trained topredict the homologous recombination status of a cancer.

Loss of homologous recombination is a widely-recognized determinant ofcancer progression. Yet, few computational resources exist to estimatehomologous recombination deficiency (HRD) from patient genomes.Genomics-based HRD testing is valuable for cancer diagnostics and couldbe used for patient stratification towards treatment with, for example,PARPi. Systems and methods are disclosed for the estimation of HRDstatus of a person's cancer.

In one aspect, the present disclosure provides a method for determininga homologous recombination pathway status of a cancer in a test subject.The method includes obtaining a first plurality of sequence reads, inelectronic form, of a first DNA sample from the test subject, the firstDNA sample including DNA molecules from a cancerous tissue of thesubject. The method includes obtaining a second plurality of sequencereads, in electronic, of a second DNA sample from the test subject, thesecond DNA sample consisting of DNA molecules from a non-canceroustissue of the subject. The method then includes generating, based on thefirst plurality of sequence reads and the second plurality of sequencereads, a genomic data construct for the subject, the genomic dataconstruct comprising one or more a features of the genomes of thecancerous and non-cancerous tissues of the subject. In some embodiments,the plurality of features includes (i) a heterozygosity status for afirst plurality of DNA damage repair genes in the genome of thecancerous tissue of the subject, (ii) a measure of the loss ofheterozygosity across the genome of the cancerous tissue of the subject,(iii) a measure of variant alleles detected in a second plurality of DNAdamage repair genes in the genome of the cancerous tissue of thesubject, and (iv) a measure of variant alleles detected in the secondplurality of DNA damage repair genes in the genome of the non-canceroustissue of the subject. The method then includes inputting the genomicdata construct into a classifier trained to distinguish between cancerswith homologous recombination pathway deficiencies and cancers withouthomologous recombination pathway deficiencies, thereby determining thehomologous recombination pathway status of the test subject.

In another aspect, the present disclosure provides a method for trainingan algorithm for determining a homologous recombination pathway statusof a cancer. The method includes obtaining, for each respective trainingsubject in a plurality of training subjects with cancer, a correspondinggenomic data construct for the respective training subject. Thecorresponding genomic training construct includes (a) a homologousrecombination pathway status for the cancer of the respective trainingsubject and (b) one or more features of the genomes of cancerous and anon-cancerous tissues of the respective training subject. In someembodiments, the one or more features includes (i) a heterozygositystatus for a first plurality of DNA damage repair genes in the genome ofthe cancerous tissue of the respective training subject, (ii) a measureof the loss of heterozygosity across the genome of the cancerous tissueof the respective training subject, (iii) a measure of variant allelesdetected in a second plurality of DNA damage repair genes in the genomeof the cancerous tissue of the respective training subject, and (iv) ameasure of variant alleles detected in the second plurality of DNAdamage repair genes in the genome of the non-cancerous tissue of therespective training subject. The method then includes training aclassification algorithm against, for each respective training subject,at least (a) the homologous recombination pathway status for the cancerof the respective training subject, and (b) the plurality of featuresdetermined from the corresponding sample of DNA from the canceroustissue of the respective training subject.

Additional aspects and advantages of the present disclosure will becomereadily apparent to those skilled in this art from the followingdetailed description, wherein only illustrative embodiments of thepresent disclosure are shown and described. As will be realized, thepresent disclosure is capable of other and different embodiments, andits several details are capable of modifications in various obviousrespects, all without departing from the disclosure. Accordingly, thedrawings and description are to be regarded as illustrative in nature,and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A and 1B collectively illustrates a block diagram of an exampleof a computing device for using information derived from DNA sequencingof cancerous tissue to predict the homologous recombination status of acancer, in accordance with some embodiments of the present disclosure.

FIG. 2 provides a flow chart of an example method for using informationderived from DNA sequencing of cancerous tissue to predict thehomologous recombination status of a cancer, in accordance with someembodiments of the present disclosure.

FIG. 3 illustrates an example of a method for generating a clinicalreport based off of information generated from analysis of one or morepatient specimens.

FIG. 4 illustrates example inputs for the HRD classification models, inaccordance with some embodiments of the disclosure.

FIG. 5 illustrates an example bioinformatics pipeline for tumor-normalmatched variant calling and tumor-only calling, in accordance with someembodiments of the present disclosure.

FIG. 6 illustrates that paired-end reads from tumor and normal isolatesare zipped and stored separately under the same order identifier, inaccordance with some embodiments of the present disclosure.

FIG. 7 illustrates quality correction for FASTQ files, in accordancewith some embodiments of the present disclosure.

FIG. 8 illustrates steps for obtaining tumor and normal BAM alignmentfiles, in accordance with some embodiments of the present disclosure.

FIG. 9 illustrates steps for calling variants from tumor and normal BAMalignment files, in accordance with some embodiments of the presentdisclosure.

FIG. 10 illustrates an example system for generating HRD calls and thenecessary output, in accordance with some embodiments of the presentdisclosure.

FIG. 11 illustrates an example display of text and images that indicateHRD information, in accordance with some embodiments of the presentdisclosure.

FIG. 12 illustrates a portion of an example report listing geneticvariants related to genes in the homologous recombination DNA repairpathway and/or genes that interact with this pathway, in accordance withsome embodiments of the present disclosure.

Like reference numerals refer to corresponding parts throughout theseveral views of the drawings.

DETAILED DESCRIPTION

The present disclosure provides systems and methods for usinginformation derived from DNA sequencing of cancerous tissue to predictthe homologous recombination status of a cancer, to improve treatmentpredictions and outcomes. In some embodiments, sequencing data frommatched cancerous tissue and germline tissue are used together toimprove the accuracy of the predictions.

Definitions

The terminology used in the present disclosure is for the purpose ofdescribing particular embodiments only and is not intended to belimiting of the invention. As used in the description of the inventionand the appended claims, the singular forms “a”, “an” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise. It will also be understood that the term “and/or”as used herein refers to and encompasses any and all possiblecombinations of one or more of the associated listed items. It will befurther understood that the terms “comprises” and/or “comprising,” whenused in this specification, specify the presence of stated features,integers, steps, operations, elements, and/or components, but do notpreclude the presence or addition of one or more other features,integers, steps, operations, elements, components, and/or groupsthereof. Furthermore, to the extent that the terms “including,”“includes,” “having,” “has,” “with,” or variants thereof are used ineither the detailed description and/or the claims, such terms areintended to be inclusive in a manner similar to the term “comprising.”

As used herein, the term “if” may be construed to mean “when” or “upon”or “in response to determining” or “in response to detecting,” dependingon the context. Similarly, the phrase “if it is determined” or “if [astated condition or event] is detected” may be construed to mean “upondetermining” or “in response to determining” or “upon detecting [thestated condition or event]” or “in response to detecting [the statedcondition or event],” depending on the context.

It will also be understood that, although the terms first, second, etc.may be used herein to describe various elements, these elements shouldnot be limited by these terms. These terms are only used to distinguishone element from another. For example, a first subject could be termed asecond subject, and, similarly, a second subject could be termed a firstsubject, without departing from the scope of the present disclosure. Thefirst subject and the second subject are both subjects, but they are notthe same subject. Furthermore, the terms “subject,” “user,” and“patient” are used interchangeably herein.

As used herein, the term “subject” refers to any living or non-livinghuman. In some embodiments, a subject is a male or female of any stage(e.g., a man, a women or a child).

As used herein, the terms “control,” “control sample,” “reference,”“reference sample,” “normal,” and “normal sample” describe a sample froma subject that does not have a particular condition, or is otherwisehealthy. In an example, a method as disclosed herein can be performed ona subject having a tumor, where the reference sample is a sample takenfrom a healthy tissue of the subject. A reference sample can be obtainedfrom the subject, or from a database. The reference can be, e.g., areference genome that is used to map sequence reads obtained fromsequencing a sample from the subject. A reference genome can refer to ahaploid or diploid genome to which sequence reads from the biologicalsample and a constitutional sample can be aligned and compared. Anexample of constitutional sample can be DNA of white blood cellsobtained from the subject. For a haploid genome, there can be only onenucleotide at each locus. For a diploid genome, heterozygous loci can beidentified; each heterozygous locus can have two alleles, where eitherallele can allow a match for alignment to the locus.

As used herein, the term “locus” refers to a position (e.g., a site)within a genome, e.g., on a particular chromosome. In some embodiments,a locus refers to a single nucleotide position within a genome, i.e., ona particular chromosome. In some embodiments, a locus refers to a smallgroup of nucleotide positions within a genome, e.g., as defined by amutation (e.g., substitution, insertion, or deletion) of consecutivenucleotides within a cancer genome. Because normal mammalian cells havediploid genomes, a normal mammalian genome (e.g., a human genome) willgenerally have two copies of every locus in the genome, or at least twocopies of every locus located on the autosomal chromosomes, e.g., onecopy on the maternal autosomal chromosome and one copy on the paternalautosomal chromosome.

As used herein, the term “allele” refers to a particular sequence of oneor more nucleotides at a chromosomal locus.

As used herein, the term “reference allele” refers to the sequence ofone or more nucleotides at a chromosomal locus that is either thepredominant allele represented at that chromosomal locus within thepopulation of the species (e.g., the “wild-type” sequence), or an allelethat is predefined within a reference genome for the species.

As used herein, the term “variant allele” refers to a sequence of one ormore nucleotides at a chromosomal locus that is either not thepredominant allele represented at that chromosomal locus within thepopulation of the species (e.g., not the “wild-type” sequence), or notan allele that is predefined within a reference genome for the species.

As used herein, the term “single nucleotide variant” or “SNV” refers toa substitution of one nucleotide to a different nucleotide at a position(e.g., site) of a nucleotide sequence, e.g., a sequence read from anindividual. A substitution from a first nucleobase X to a secondnucleobase Y may be denoted as “X>Y.” For example, a cytosine to thymineSNV may be denoted as “C>T.”

As used herein, the term “mutation” or “variant” refers to a detectablechange in the genetic material of one or more cells. In a particularexample, one or more mutations can be found in, and can identify, cancercells (e.g., driver and passenger mutations). A mutation can betransmitted from apparent cell to a daughter cell. A person having skillin the art will appreciate that a genetic mutation (e.g., a drivermutation) in a parent cell can induce additional, different mutations(e.g., passenger mutations) in a daughter cell. A mutation generallyoccurs in a nucleic acid. In a particular example, a mutation can be adetectable change in one or more deoxyribonucleic acids or fragmentsthereof. A mutation generally refers to nucleotides that is added,deleted, substituted for, inverted, or transposed to a new position in anucleic acid. A mutation can be a spontaneous mutation or anexperimentally induced mutation. A mutation in the sequence of aparticular tissue is an example of a “tissue-specific allele.” Forexample, a tumor can have a mutation that results in an allele at alocus that does not occur in normal cells. Another example of a“tissue-specific allele” is a fetal-specific allele that occurs in thefetal tissue, but not the maternal tissue.

As used herein, the term “loss of heterozygosity” refers to the loss ofone copy of a segment (e.g., including part or all of one or more genes)of the genome of a diploid subject (e.g., a human) or loss of one copyof a sequence encoding a functional gene product in the genome of thediploid subject, in a tissue, e.g., a cancerous tissue, of the subject.As used herein, when referring to a metric representing loss ofheterozygosity across the entire genome of the subject, loss ofheterozygosity is caused by the loss of one copy of various segments inthe genome of the subject. Loss of heterozygosity across the entiregenome may be estimated without sequencing the entire genome of asubject, and such methods for such estimations based on gene paneltargeting-based sequencing methodologies are described in the art.Accordingly, in some embodiments, a metric representing loss ofheterozygosity across the entire genome of a tissue of a subject isrepresented as a single value, e.g., a percentage or fraction of thegenome. In some cases a tumor is composed of various sub-clonalpopulations, each of which may have a different degree of loss ofheterozygosity across their respective genomes. Accordingly, in someembodiments, loss of heterozygosity across the entire genome of acancerous tissue refers to an average loss of heterozygosity across aheterogeneous tumor population. As used herein, when referring to ametric for loss of heterozygosity in a particular gene, e.g., a DNArepair protein such as a protein involved in the homologous DNArecombination pathway (e.g., BRCA1 or BRCA2), loss of heterozygosityrefers to complete or partial loss of one copy of the gene encoding theprotein in the genome of the tissue and/or a mutation in one copy of thegene that prevents translation of a full-length gene product, e.g., aframeshift or truncating (creating a premature stop codon in the gene)mutation in the gene of interest. In some cases a tumor is composed ofvarious sub-clonal populations, each of which may have a differentmutational status in a gene of interest. Accordingly, in someembodiments, loss of heterozygosity for a particular gene of interest isrepresented by an average value for loss of heterozygosity for the geneacross all sequenced sub-clonal populations of the cancerous tissue. Inother embodiments, loss of heterozygosity for a particular gene ofinterest is represented by a count of the number of unique incidences ofloss of heterozygosity in the gene of interest across all sequencedsub-clonal populations of the cancerous tissue (e.g., the number ofunique frame-shift and/or truncating mutations in the gene identified inthe sequencing data).

As used herein the term “cancer,” “cancerous tissue,” or “tumor” refersto an abnormal mass of tissue in which the growth of the mass surpassesand is not coordinated with the growth of normal tissue. A cancer ortumor can be defined as “benign” or “malignant” depending on thefollowing characteristics: degree of cellular differentiation includingmorphology and functionality, rate of growth, local invasion andmetastasis. A “benign” tumor can be well differentiated, havecharacteristically slower growth than a malignant tumor and remainlocalized to the site of origin. In addition, in some cases a benigntumor does not have the capacity to infiltrate, invade or metastasize todistant sites. A “malignant” tumor can be a poorly differentiated(anaplasia), have characteristically rapid growth accompanied byprogressive infiltration, invasion, and destruction of the surroundingtissue. Furthermore, a malignant tumor can have the capacity tometastasize to distant sites. Accordingly, a cancer cell is a cell foundwithin the abnormal mass of tissue whose growth is not coordinated withthe growth of normal tissue. Accordingly, a “tumor sample” refers to abiological sample obtained or derived from a tumor of a subject, asdescribed herein.

As used herein, the terms “sequencing,” “sequence determination,” andthe like as used herein refers generally to any and all biochemicalprocesses that may be used to determine the order of biologicalmacromolecules such as nucleic acids or proteins. For example,sequencing data can include all or a portion of the nucleotide bases ina nucleic acid molecule such as an mRNA transcript or a genomic locus.

As used herein, the term “sequence reads” or “reads” refers tonucleotide sequences produced by any sequencing process described hereinor known in the art. Reads can be generated from one end of nucleic acidfragments (“single-end reads”), and sometimes are generated from bothends of nucleic acids (e.g., paired-end reads, double-end reads). Thelength of the sequence read is often associated with the particularsequencing technology. High-throughput methods, for example, providesequence reads that can vary in size from tens to hundreds of base pairs(bp). In some embodiments, the sequence reads are of a mean, median oraverage length of about 15 bp to 900 bp long (e.g., about 20 bp, about25 bp, about 30 bp, about 35 bp, about 40 bp, about 45 bp, about 50 bp,about 55 bp, about 60 bp, about 65 bp, about 70 bp, about 75 bp, about80 bp, about 85 bp, about 90 bp, about 95 bp, about 100 bp, about 110bp, about 120 bp, about 130, about 140 bp, about 150 bp, about 200 bp,about 250 bp, about 300 bp, about 350 bp, about 400 bp, about 450 bp, orabout 500 bp. In some embodiments, the sequence reads are of a mean,median or average length of about 1000 bp, 2000 bp, 5000 bp, 10,000 bp,or 50,000 bp or more. Nanopore sequencing, for example, can providesequence reads that can vary in size from tens to hundreds to thousandsof base pairs. Illumina parallel sequencing can provide sequence readsthat do not vary as much, for example, most of the sequence reads can besmaller than 200 bp. A sequence read (or sequencing read) can refer tosequence information corresponding to a nucleic acid molecule (e.g., astring of nucleotides). For example, a sequence read can correspond to astring of nucleotides (e.g., about 20 to about 150) from part of anucleic acid fragment, can correspond to a string of nucleotides at oneor both ends of a nucleic acid fragment, or can correspond tonucleotides of the entire nucleic acid fragment. A sequence read can beobtained in a variety of ways, e.g., using sequencing techniques orusing probes, e.g., in hybridization arrays or capture probes, oramplification techniques, such as the polymerase chain reaction (PCR) orlinear amplification using a single primer or isothermal amplification.

As used herein, the term “read segment” or “read” refers to anynucleotide sequences including sequence reads obtained from anindividual and/or nucleotide sequences derived from the initial sequenceread from a sample obtained from an individual. For example, a readsegment can refer to an aligned sequence read, a collapsed sequenceread, or a stitched read. Furthermore, a read segment can refer to anindividual nucleotide base, such as a single nucleotide variant.

As used herein, the term, “reference exome” refers to any particularknown, sequenced or characterized exome, whether partial or complete, ofany tissue from any organism or pathogen that may be used to referenceidentified sequences from a subject. Example reference exomes used forhuman subjects as well as many other organisms are provided in theon-line genome browser hosted by the National Center for BiotechnologyInformation (“NCBI”).

As used herein, the term “reference genome” refers to any particularknown, sequenced or characterized genome, whether partial or complete,of any organism or pathogen that may be used to reference identifiedsequences from a subject. Exemplary reference genomes used for humansubjects as well as many other organisms are provided in the on-linegenome browser hosted by the National Center for BiotechnologyInformation (“NCBI”) or the University of California, Santa Cruz (UCSC).A “genome” refers to the complete genetic information of an organism orpathogen, expressed in nucleic acid sequences. As used herein, areference sequence or reference genome often is an assembled orpartially assembled genomic sequence from an individual or multipleindividuals. In some embodiments, a reference genome is an assembled orpartially assembled genomic sequence from one or more human individuals.The reference genome can be viewed as a representative example of aspecies' set of genes. In some embodiments, a reference genome comprisessequences assigned to chromosomes. Exemplary human reference genomesinclude but are not limited to NCBI build 34 (UCSC equivalent: hg16),NCBI build 35 (UCSC equivalent: hg17), NCBI build 36.1 (UCSC equivalent:hg18), GRCh37 (UCSC equivalent: hg19), and GRCh38 (UCSC equivalent:hg38).

As used herein, the term “assay” refers to a technique for determining aproperty of a substance, e.g., a nucleic acid, a protein, a cell, atissue, or an organ. An assay (e.g., a first assay or a second assay)can comprise a technique for determining the copy number variation ofnucleic acids in a sample, the methylation status of nucleic acids in asample, the fragment size distribution of nucleic acids in a sample, themutational status of nucleic acids in a sample, or the fragmentationpattern of nucleic acids in a sample. Any assay known to a person havingordinary skill in the art can be used to detect any of the properties ofnucleic acids mentioned herein. Properties of a nucleic acids caninclude a sequence, genomic identity, copy number, methylation state atone or more nucleotide positions, size of the nucleic acid, presence orabsence of a mutation in the nucleic acid at one or more nucleotidepositions, and pattern of fragmentation of a nucleic acid (e.g., thenucleotide position(s) at which a nucleic acid fragments). An assay ormethod can have a particular sensitivity and/or specificity, and theirrelative usefulness as a diagnostic tool can be measured using ROC-AUCstatistics.

The term “classification” can refer to any number(s) or othercharacters(s) that are associated with a particular property of asample. For example, in some embodiments, the term “classification” canrefer to a type of cancer in a subject or sample, a stage of cancer in asubject or sample, a prognosis for a cancer in a subject or sample, atumor load in a subject, a presence of tumor metastasis in a subject,and the like. The classification can be binary (e.g., positive ornegative) or have more levels of classification (e.g., a scale from 1 to10 or 0 to 1). The terms “cutoff” and “threshold” can refer topredetermined numbers used in an operation. For example, a cutoff sizecan refer to a size above which fragments are excluded. A thresholdvalue can be a value above or below which a particular classificationapplies. Either of these terms can be used in either of these contexts.

Several aspects are described below with reference to exampleapplications for illustration. It should be understood that numerousspecific details, relationships, and methods are set forth to provide afull understanding of the features described herein. One having ordinaryskill in the relevant art, however, will readily recognize that thefeatures described herein can be practiced without one or more of thespecific details or with other methods. The features described hereinare not limited by the illustrated ordering of acts or events, as someacts can occur in different orders and/or concurrently with other actsor events. Furthermore, not all illustrated acts or events are requiredto implement a methodology in accordance with the features describedherein.

Reference will now be made in detail to embodiments, examples of whichare illustrated in the accompanying drawings. In the following detaileddescription, numerous specific details are set forth in order to providea thorough understanding of the present disclosure. However, it will beapparent to one of ordinary skill in the art that the present disclosuremay be practiced without these specific details. In other instances,well-known methods, procedures, components, circuits, and networks havenot been described in detail so as not to unnecessarily obscure aspectsof the embodiments.

Example System Embodiments.

A detailed description of a system 100 for determining a homologousrecombination pathway status of a cancer in a test subject and/ortraining an algorithm for determining a homologous recombination pathwaystatus of a cancer is described in conjunction with FIGS. 1A-1B. Assuch, FIGS. 1A-1B collectively illustrate the topology of a system, inaccordance with an embodiment of the present disclosure.

Referring to FIG. 1A, in typical embodiments, system 100 includes one ormore computers. For purposes of illustration in FIG. 1A, system 100 isrepresented as a single computer that includes all of the functionalityfor identifying interactions within complex biological systems usingdata from a cell-based assay. However, in some embodiments, thefunctionality for determining the homologous recombination pathwaystatus of a cancer in a test subject is spread across any number ofnetworked computers and/or resides on each of several networkedcomputers and/or is hosted on one or more virtual machines at a remotelocation accessible across the communications network 105. One of skillin the art will appreciate that any of a wide array of differentcomputer topologies are used for the application and all such topologiesare within the scope of the present disclosure.

Details of an example system are now described in conjunction withFIG. 1. FIG. 1 is a block diagram illustrating a system 100 inaccordance with some implementations. Device 100 in some implementationsincludes at least one or more processing units CPU(s) 102 (also referredto as processors), one or more network interfaces 104, a user interface106, e.g., including a display 108 and/or keyboard 110, a memory 111,and one or more communication buses 114 for interconnecting thesecomponents. The one or more communication buses 114 optionally includecircuitry (sometimes called a chipset) that interconnects and controlscommunications between system components. The memory 111 may be anon-persistent memory, a persistent memory 112, or any combinationthereof. The non-persistent memory typically includes high-speed randomaccess memory, such as DRAM, SRAM, DDR RAM, ROM, EEPROM, flash memory,whereas the persistent memory typically includes CD-ROM, digitalversatile disks (DVD) or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,magnetic disk storage devices, optical disk storage devices, flashmemory devices, or other non-volatile solid state storage devices.Regardless of its specific implementation, the memory 111 comprises atleast one non-transitory computer readable storage medium, and it storesthereon computer-executable executable instructions which can be in theform of programs, modules, and data structures.

In some embodiments, as shown in FIG. 1A, the memory 111 stores:

-   -   an operating system 116, which includes procedures for handling        various basic system services and for performing        hardware-dependent tasks;    -   an optional network communication module (or instructions) 118        for connecting the system 100 with other devices and/or to a        communication network 105;    -   a first test dataset 120-1 comprising a first plurality of        sequence reads 122 (e.g., 122-1-1, . . . 122-1-N), in electronic        form, of a first DNA sample from the test subject, the first DNA        sample comprising DNA molecules from a cancerous tissue of the        subject;    -   a second test dataset 120-2 comprising a second plurality of        sequence reads 122 (e.g., 122-2-1, . . . , 122-2-M), in        electronic form, of a second DNA sample from the test subject,        the second DNA sample consisting of DNA molecules from a        non-cancerous tissue of the subject;    -   a test genomic data construct 128 that is generated based on the        first plurality of sequence reads and the second plurality of        sequence reads, comprising one or more features of the genomes        of the cancerous and non-cancerous tissues of the subject that        can be inputted into a classifier trained to distinguish between        cancers with homologous recombination pathway deficiencies and        cancers without homologous recombination pathway deficiencies,        comprising:        -   as illustrated in FIG. 1B, for a first plurality of DNA            damage repair genes 130-1, a heterozygosity status in the            genome of the cancerous tissue of the subject (e.g., the            first dataset) 132;        -   a measure of the loss of heterozygosity across the genome of            the cancerous tissue of the subject (e.g., the first            dataset) 134, where the measure of the loss of            heterozygosity across the genome of the cancerous tissue of            the subject is optionally determined by determining a loss            of genomic heterozygosity in the first plurality of sequence            reads 136, and normalizing the determined loss of            heterozygosity by an estimate of the tumor purity for the            first plurality of sequence reads 138;        -   for a second plurality of DNA damage repair genes 130-2, a            measure of variant alleles detected in the genome of the            cancerous tissue of the subject (e.g., the first dataset)            140-1; and        -   for the second plurality of DNA damage repair genes 130-2, a            measure of variant alleles detected in the genome of the            non-cancerous tissue of the subject (e.g., the second            dataset) 140-2;    -   a classifier training module 170 for training disease        classifiers 173 to distinguish between disease states, e.g.,        using training data stored in training genomic data construct        176;    -   disease classifiers 173, e.g., one or more homologous        recombination pathway classifiers 174 for distinguishing between        cancers with homologous recombination pathway deficiencies and        cancers without homologous recombination pathway deficiencies;    -   a classifier evaluation module 171 for evaluating a disease        classifier;    -   a disease classification module 172 for determining the        homologous recombination pathway status of a test subject, e.g.,        by evaluating test genomic data construct 128 with a trained        disease classifier 173; and    -   a training genomic data construct 176 for a respective training        subject, storing training genomic data that can be used to train        an algorithm, e.g., disease classifiers 173, to determine a        homologous recombination pathway status of a cancer, comprising        a homologous recombination pathway status 190 for the cancer of        the respective training subject and one or more features of the        genomes of cancerous and non-cancerous tissues of the respective        training subject, including:        -   as illustrated in FIG. 1B, for a first plurality of DNA            damage repair genes 178-1, a heterozygosity status in the            genome of the cancerous tissue of the subject 180;        -   a measure of the loss of heterozygosity across the genome of            the cancerous tissue of the subject 182, where the measure            of the loss of heterozygosity across the genome of the            cancerous tissue of the subject is optionally determined by            determining a loss of genomic heterozygosity in the first            plurality of sequence reads 184, and normalizing the            determined loss of heterozygosity by an estimate of the            tumor purity for the first plurality of sequence reads 186;        -   for a second plurality of DNA damage repair genes 178-2, a            measure of variant alleles detected in the genome of the            cancerous tissue of the subject 188-1; and        -   for the second plurality of DNA damage repair genes 178-2, a            measure of variant alleles detected in the genome of the            non-cancerous tissue of the subject 188-2.

In some implementations, modules 118, 170, 171 and/or 172 and/or datastores 120, 128 and/or 176 are accessible within any browser (e.g.,installed on a phone, tablet, or laptop/desktop system). In someembodiments, modules 118, 120, 170, 171 and/or 172 run on native deviceframeworks, and are available for download onto the system 100 runningan operating system 116, such as Windows, macOS, a Linux operatingsystem, Android OS, or iOS.

In some implementations, one or more of the above identified dataelements or modules of the system 100 are stored in one or more of thepreviously described memory devices, and correspond to a set ofinstructions for performing a function described above. Theabove-identified data, modules or programs (e.g., sets of instructions)need not be implemented as separate software programs, procedures ormodules, and thus various subsets of these modules may be combined orotherwise re-arranged in various implementations. In someimplementations, the memory 111 optionally stores a subset of themodules and data structures identified above. Furthermore, in someembodiments the memory 111 stores additional modules and data structuresnot described above. In some embodiments, one or more of theabove-identified elements is stored in a computer system, other thanthat of system 100, that is addressable by system 100 so that system 100may retrieve all or a portion of such data when needed.

Although FIG. 1 depicts a “system 100,” the figure is intended as afunctional description of the various features that may be present incomputer systems than as a structural schematic of the implementationsdescribed herein. In practice, and as recognized by those of ordinaryskill in the art, items shown separately could be combined and someitems can be separate. Moreover, although FIG. 1 depicts certain dataand modules in the memory 111 (which can be non-persistent 111 orpersistent memory 112), it should be appreciated that these data andmodules, or portion(s) thereof, may be stored in more than one memory.

Example Methods

Now that details of a system 100 for determining a homologousrecombination pathway status of a cancer in a test subject and/ortraining an algorithm for determining a homologous recombination pathwaystatus of a cancer have been disclosed, details regarding processes andfeatures of the system, in accordance with various embodiment of thepresent disclosure, are disclosed below. Specifically, example processesare described below with reference to FIG. 2. In some embodiments, suchprocesses and features of the system are carried out by modules 118,120, 170, 171 and/or 172, as illustrated in FIG. 1. Referring to thesemethods, the systems described herein (e.g., system 100) includeinstructions for determining a homologous recombination pathway statusof a cancer in a test subject and/or training an algorithm fordetermining a homologous recombination pathway status of a cancer.

FIG. 2 illustrates an example workflow 200 for determining a homologousrecombination pathway status of a cancer in a test subject, inaccordance with various embodiments of the present disclosure. Furtherdetails on various implementation of the steps illustrated in workflow200 are described with more particularity below. The skilled artisanwill know of suitable alternatives for performing each of the stepsshown in workflow 200.

In one aspect, the disclosure provides a method 200 for determining ahomologous recombination pathway status of a cancer in a test subject.The method includes obtaining (202) a first plurality of sequence reads,in electronic form, of a first DNA sample from the test subject, thefirst DNA sample including DNA molecules from a cancerous tissue of thesubject. The method includes obtaining (204) a second plurality ofsequence reads, in electronic, of a second DNA sample from the testsubject, the second DNA sample consisting of DNA molecules from anon-cancerous tissue of the subject.

In some embodiments, the first DNA sample is from a solid tumor biopsyof the cancerous tissue of the subject. In other embodiments, the secondDNA sample is from a liquid sample, e.g., a liquid biopsy. Generally, acancerous biological sample of the subject is a biopsy. Methods forobtaining samples of cancerous tissue are known in the art, and aredependent upon the type of cancer being sampled. For example, bonemarrow biopsies and isolation of circulating tumor cells can be used toobtain samples of blood cancers, endoscopic biopsies can be used toobtain samples of cancers of the digestive tract, bladder, and lungs,needle biopsies (e.g., fine-needle aspiration, core needle aspiration,vacuum-assisted biopsy, and image-guided biopsy, can be used to obtainsamples of subdermal tumors, skin biopsies, e.g., shave biopsy, punchbiopsy, incisional biopsy, and excisional biopsy, can be used to obtainsamples of dermal cancers, and surgical biopsies can be used to obtainsamples of cancers affecting internal organs of a patient. In someembodiments, the biological sample is a solid biopsy. In someembodiments, the solid biopsy is a macro-dissected formalin fixedparaffin embedded (FFPE) tissue section. In some embodiments, thebiological sample comprises blood or saliva.

In some embodiments, the first plurality of sequence reads was generatedby targeted sequencing using a plurality of nucleic acid probes toenrich nucleic acids from the cancerous tissue of the subject for apanel of genomic regions. In some embodiments, the first plurality ofsequence reads was generated by whole genome sequencing of nucleic acidsfrom the cancerous tissue of the subject. In some embodiments, firstplurality of sequence reads was generated by whole or partial exomesequencing of nucleic acids from the cancerous tissue of the subject.

In some embodiments, the second DNA sample is from a buffy coatpreparation of a blood sample from the subject. In other embodiments,the second DNA sample is from saliva of the subject. Generally, anysample containing genomic or exomic material which is substantially allderived from non-cancerous tissues can be used to generate the secondplurality of sequence reads.

In some embodiments, the second plurality of sequence reads wasgenerated by targeted sequencing using a plurality of nucleic acidprobes to enrich nucleic acids from the non-cancerous tissue of thesubject for a panel of genomic regions. In some embodiments, the secondplurality of sequence reads was generated by whole genome sequencing ofnucleic acids from the non-cancerous tissue of the subject. In someembodiments, second plurality of sequence reads was generated by wholeor partial exome sequencing of nucleic acids from the non-canceroustissue of the subject.

The method then includes generating (206), based on the first pluralityof sequence reads and the second plurality of sequence reads, a genomicdata construct for the subject, the genomic data construct comprisingone or more a features of the genomes of the cancerous and non-canceroustissues of the subject. In some embodiments, the plurality of featuresincludes (i) a heterozygosity status for a first plurality of DNA damagerepair genes in the genome of the cancerous tissue of the subject, (ii)a measure of the loss of heterozygosity across the genome of thecancerous tissue of the subject, (iii) a measure of variant allelesdetected in a second plurality of DNA damage repair genes in the genomeof the cancerous tissue of the subject, and (iv) a measure of variantalleles detected in the second plurality of DNA damage repair genes inthe genome of the non-cancerous tissue of the subject.

In some embodiments, the measure of the loss of heterozygosity acrossthe genome of the cancerous tissue of the subject is obtained bydetermining a loss of genomic heterozygosity in the first plurality ofsequence reads, and normalizing the determined loss of heterozygosity byan estimate of the tumor purity for the first plurality of sequencereads. That is, many ‘tumor biopsies’ contain a residual percentage ofnon-cancerous cells. When estimating the loss of heterozygosity fromnucleic acids isolated from the tumor biopsy, the presence of nucleicacids from the non-cancerous cells will skew the overall loss ofheterozygosity downwards. By estimating the tumor purity of the sample,e.g., the percentage of nucleic acids that are derived from cancerouscells rather than non-cancerous cells, the presence of non-cancerouscontributions to the sequencing data can be accounted for, providing amore accurate estimate of the loss of heterozygosity across the cancergenome of the subject.

In some embodiments, the heterozygosity status for the first pluralityof DNA damage repair genes comprises a count of the number of uniqueframeshift mutations detected in the first plurality of DNA damagerepair genes. In some embodiments, the heterozygosity status for thefirst plurality of DNA damage repair genes comprises a count of thenumber of unique truncating mutations detected in the first plurality ofDNA damage repair genes. In some embodiments, the first plurality of DNAdamage repair genes are genes involved in the homologous recombinationpathway. In some embodiments, the first plurality of DNA damage repairgenes includes BRCA1 and BRCA2.

In some embodiments, the measure of variant alleles detected in thesecond plurality of DNA damage repair genes in the genome of thecancerous tissue of the subject includes a count of the number of uniquemutations associated with loss of homologous recombination detected inthe first plurality of sequence reads. In some embodiments, the measureof variant alleles detected in the second plurality of DNA damage repairgenes in the genome of the non-cancerous tissue of the subject includesa count of the number of unique mutations associated with loss ofhomologous recombination detected in the second plurality of sequencereads.

In some embodiments, the second plurality of DNA damage repair genes aregenes involved in the homologous recombination pathway. In someembodiments, the second plurality of DNA damage repair genes includeBRCA1 and BRCA2. In some embodiments, the unique mutations associatedwith loss of homologous recombination in BRCA1 and BRCA2 include atleast 25, 50, 75, 100, 125, or all of the mutations listed in Table 1.

The method then includes inputting (208) the genomic data construct intoa classifier trained to distinguish between cancers with homologousrecombination pathway deficiencies and cancers without homologousrecombination pathway deficiencies, thereby determining the homologousrecombination pathway status of the test subject. In some embodiments,the classifier is a neural network algorithm, a support vector machinealgorithm, a Naive Bayes algorithm, a nearest neighbor algorithm, aboosted trees algorithm, a random forest algorithm, a convolutionalneural network algorithm, a decision tree algorithm, a regressionalgorithm, or a clustering algorithm, as described in further detailbelow.

In some embodiments, method 200 also includes treating the subject basedon the HRD prediction made by the classifier. For example, in someembodiments, when it is determined that the cancer in the test subjectis homologous recombination deficient, treating the cancer byadministering a poly ADP ribose polymerase (PARP) inhibitor to the testsubject, and when it is determined the cancer in the test subject is nothomologous recombination deficient, treating the cancer with a therapythat does not include administration of a PARP inhibitor to the testsubject. In some embodiments, the PARP inhibitor is selected fromolaparib, veliparib, rucaparib, niraparib, and talazoparib. A summary ofcurrent FDA approvals for various PARP inhibitors is provided in Table2, below.

In another aspect, the present disclosure provides a method for trainingan algorithm for determining a homologous recombination pathway statusof a cancer. The method includes obtaining, for each respective trainingsubject in a plurality of training subjects with cancer, a correspondinggenomic data construct for the respective training subject. Thecorresponding genomic training construct includes (a) a homologousrecombination pathway status for the cancer of the respective trainingsubject and (b) one or more features of the genomes of cancerous and anon-cancerous tissues of the respective training subject. In someembodiments, the one or more features includes (i) a heterozygositystatus for a first plurality of DNA damage repair genes in the genome ofthe cancerous tissue of the respective training subject, (ii) a measureof the loss of heterozygosity across the genome of the cancerous tissueof the respective training subject, (iii) a measure of variant allelesdetected in a second plurality of DNA damage repair genes in the genomeof the cancerous tissue of the respective training subject, and (iv) ameasure of variant alleles detected in the second plurality of DNAdamage repair genes in the genome of the non-cancerous tissue of therespective training subject. The method then includes training aclassification algorithm against, for each respective training subject,at least (a) the homologous recombination pathway status for the cancerof the respective training subject, and (b) the plurality of featuresdetermined from the corresponding sample of DNA from the canceroustissue of the respective training subject.

FIG. 3 displays a flowchart of an exemplary method for generating aclinical report based off information generated from analysis of one ormore patient specimens and ingestion of the patient's healthinformation. A clinical laboratory may receive an order, such as anorder for comprehensive genomic profiling or an order for a test thatprovides an estimate of HRD status. Physical specimens may be providedto a laboratory for processing and analysis. The processing and analysismay include analysis may nucleotide and clinical information that mayinclude an estimate of HRD status. The one or more specimens may beprocessed through a laboratory, which may include the steps ofaccessioning, pathology review, extraction, library prep, capture andhybridization, pooling, and sequencing. Sequencing may be performedusing next generation sequencing technologies, such as short-readtechnologies. Other sequencing methods, such as long-read sequencing orother sequencing methods known in the art, alternately may be used.Sequencing results may be provided to a bioinformatics pipeline. Theresults of the bioinformatics pipeline may be provided for variantscience analysis, including the interpretation of variants (includingsomatic and germline variants as applicable) for pathogenic andbiological significance. The variant science analysis may also estimatemicrosatellite instability (MSI) or tumor mutational burden. Targetedtreatments may be identified based on gene, variant, and cancer type,for further consideration and review by the ordering physician. In someaspects, clinical trials may be identified for which the patient may beeligible, based on mutations, cancer type, and/or clinical history. Avalidation step may occur, after which the report may be finalized forsignout and delivery. In some aspects, the report includes an estimateof HRD status. In other aspects, a second report may be delivered thathas an estimate of HRD status, based on the information produced inparts of the method presented in FIG. 3.

Biological Samples

In some embodiments, the estimated HRD status may be generated based oninformation about the nucleotides of cancer and/or normal specimens. Thecancer specimen may be from a cancer of different subtypes, includinghematological and solid tumors. In some embodiments, the sample typeutilized for comprehensive genomic profiling may be fixed formalin,paraffin embedded (FFPE) slides, peripheral blood, or bone marrowaspirate. The samples may be collected in a repository such a potassiumethylenediaminetetraacetic acid (EDTA) tube. The specimen may be atissue block or a plurality of FFPE slides, such as up to 3 slides, upto 5 slides, up to 10 slides, or up to 20 slides. In some embodiments,the matched normal specimen is peripheral blood or saliva.

Features

In some aspects, the information used to produce an estimated HRD statusmay be produced by sequencing conducted by a multi-gene comprehensivegenomic profiling panel. The panel may analyze more than 10, more than100, or more than 1,000 genes. The panel may be a whole-exome panel thatanalyzes the exomes of a specimen. The panel may be a whole-genome panelthat analyzes the genome of a specimen. In some aspects, the informationused to produce an estimated HRD status may be produced as part of acomprehensive genomic profiling test, such as a DNA-based test. Thepanel may identify single nucleotide variations (SNVs),insertions/deletions, copy number variations (CNVs), and generearrangements.

The systems and methods may take into account the mutational status ofcertain genes. For example, the systems and methods may take intoaccount mutational status of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,14, or 15 genes. The systems and methods may take into accountmutational status of between 15-30 genes, between 30-45 genes, between45-60 genes, between 60-75 genes, between 75-105 genes, and between1-700 genes. The systems and methods may take into account commonlymutated genes in a pathway, such as the HR-pathway (HomologousRecombination Repair Mutations HRRm).

The systems and methods may employ a panel having a sensitivity of atleast 99% for for base substitutions in at least 5% of mutant allelefraction; at least 98% for indels in at least 5% mutant allele fraction;at least 95% for CNVs from 8 or more gene copies in 30% or greater tumornuclei, and/or at least 99% for gene arrangements,

The panel may have an average sequencing depth of 500× for tumor. Thepanel may have an average sequencing depth of 150× for matched normal.

In some aspects, a report may be returned to a clinician withcomprehensive genomic profiling information, such as information aboutthe mutational status of a patient's cancer, as well as an estimate ofHRD status. In some aspects, genes reported in the comprehensive genomicprofiling information may be highlighted as underlying or otherwiserelated to the estimate of HRD status. The number of such genes may bebetween 1-5, between 1-10, between 1-20, between 1-30, between 1-40,between 1-50, and so forth. In some aspects, the genes reported asmutated in the comprehensive genomic profiling information may behighlighted as being germline or somatic alterations, where detected.

In some aspects, the systems and methods are scalable and may beutilized to permit integration with other genes in the DNA damage repairpathway, or other data-types such as RNA expression to provide clinicaldecision support with respect to treatment options, such as PARPinhibitor treatment options.

In the bioinformatics pipeline, various features may be generated thatmay be provided to an HRD prediction engine. In some embodiments, someor all of copy number segments, truncating and stop-gained effectpathogenic mutations in BRCA genes of interest, genome-wide LOHproportion, tumor purity and LOH in BRCA genes are used infer HRDstatus.

Tumor-normal matched sequencing analysis of patient specimens on agenetic sequencing panel and a subsequent bioinformatics pipeline may beused to call SNPs and copy number variants for each patient, which maybe stored in a DNA variant data set.

Each DNA variant data set may be generated by processing a cancer sampleand a non-cancer sample from the same patient through DNA whole exomenext generation sequencing (NGS) to generate DNA sequencing data, andthe DNA sequencing data may be processed by a bioinformatics pipeline togenerate a DNA variant call file (among other outputs) for each sample.The cancer sample may be a tissue sample or blood sample containingcancer cells. In some instances, a tumor organoid sample may beprocessed instead of the patient cancer sample.

In more detail, germline (“normal”, non-cancerous) DNA may be extractedfrom either blood (for example, if a patient has cancer that is not ablood cancer) or saliva (for example, if a patient has blood cancer).Normal blood samples may be collected from patients (for example, inPAXgene Blood DNA Tubes) and saliva samples may be collected frompatients (for example, in Oragene DNA Saliva Kits).

Blood cancer samples may be collected from patients (for example, inEDTA collection tubes). Macrodissected FFPE tissue sections (which maybe mounted on a histopathology slide) from solid tumor samples may beanalyzed by pathologists to determine overall tumor amount in the sampleand percent tumor cellularity as a ratio of tumor to normal nuclei. Foreach section, background tissue may be excluded or removed such that thesection meets a tumor purity threshold (in one example, at least 20% ofthe nuclei in the section are tumor nuclei).

Then, DNA may be isolated from blood samples, saliva samples, and tissuesections using commercially available reagents, including proteinase Kto generate a liquid solution of DNA.

Each solution of isolated DNA may be subjected to a quality controlprotocol to determine the concentration and/or quantity of the DNAmolecules in the solution, which may include the use of a fluorescentdye and a fluorescence microplate reader, standard spectrofluorometer,or filter fluorometer.

For each cancer sample and each normal sample, isolated DNA moleculesmay be mechanically sheared to an average length using an ultrasonicator(for example, a Covaris ultrasonicator). The DNA molecules may also beanalyzed to determine their fragment size, which may be done through gelelectrophoresis techniques and may include the use of a device such as aLabChip GX Touch.

DNA libraries may be prepared from the isolated DNA, for example, usingthe KAPA Hyper Prep Kit, a New England Biolabs (NEB) kit, or a similarkit. DNA library preparation may include the ligation of adapters ontothe DNA molecules. For example, UDI adapters, including Roche SeqCapdual end adapters, or UMI adapters (for example, full length or stubby Yadapters) may be ligated to the DNA molecules.

In this example, adapters are nucleic acid molecules that may serve asbarcodes to identify DNA molecules according to the sample from whichthey were derived and/or to facilitate the downstream bioinformaticsprocessing and/or the next generation sequencing reaction. The sequenceof nucleotides in the adapters may be specific to a sample in order todistinguish samples. The adapters may facilitate the binding of the DNAmolecules to anchor oligonucleotide molecules on the sequencer flow celland may serve as a seed for the sequencing process by providing astarting point for the sequencing reaction.

DNA libraries may be amplified and purified using reagents, for example,Axygen MAG PCR clean up beads. Then the concentration and/or quantity ofthe DNA molecules may be quantified using a fluorescent dye and afluorescence microplate reader, standard spectrofluorometer, or filterfluorometer.

DNA libraries may be pooled (two or more DNA libraries may be mixed tocreate a pool) and treated with reagents to reduce off-target capture,for example Human COT-1 and/or IDT xGen Universal Blockers. Pools may bedried in a vacufuge and resuspended. DNA libraries or pools may behybridized to a probe set (for example, a probe set specific to a panelthat includes approximately 100, 600, 1,000, 10,000, etc. of the 19,000known human genes) and amplified with commercially available reagents(for example, the KAPA HiFi HotStart ReadyMix).

Pools may be incubated in an incubator, PCR machine, water bath, orother temperature modulating device to allow probes to hybridize. Poolsmay then be mixed with Streptavidin-coated beads or another means forcapturing hybridized DNA-probe molecules, such as DNA moleculesrepresenting exons of the human genome and/or genes selected for agenetic panel.

Pools may be amplified and purified more than once using commerciallyavailable reagents, for example, the KAPA HiFi Library Amplification kitand Axygen MAG PCR clean up beads, respectively. The pools or DNAlibraries may be analyzed to determine the concentration or quantity ofDNA molecules, for example by using a fluorescent dye (for example,PicoGreen pool quantification) and a fluorescence microplate reader,standard spectrofluorometer, or filter fluorometer.

In one example, the DNA library preparation and/or whole exome capturesteps may be performed with an automated system, using a liquid handlingrobot (for example, a SciClone NGSx).

The library amplification may be performed on a device, for example, anIllumina C-Bot2, and the resulting flow cell containing amplifiedtarget-captured DNA libraries may be sequenced on a next generationsequencer, for example, an Illumina HiSeq 4000 or an Illumina NovaSeq6000 to a unique on-target depth selected by the user, for example,300×, 400×, 500×, 10,000×, etc. Samples may be further assessed foruniformity with each sample required to have 95% of all targeted bpsequenced to a minimum depth selected by the user, for example, 300×.The next generation sequencer may generate a FASTQ, BCL, or other filefor each flow cell or each patient sample.

Bioinformatics Pipeline

In certain aspects, the bioinformatics pipeline includes the systems andmethods disclosed in this document.

FASTQ and Alignment

When a matched normal tissue is available for a patient, a tumor-normalmatched sequencing run is performed. DNA is extracted from the normaltissue, typically blood or saliva. This is then sequenced in addition tothe DNA extracted from the tumor tissue. These two sequencing runs, onefor the tumor tissue, and one for the normal tissue, produce two FASTQoutput files. FASTQ format is a text-based format for storing both abiological sequence, such as nucleotide sequence, and its correspondingquality scores. These FASTQ files are analyzed to determine what geneticvariants or copy number changes are present in the sample. A ‘matched’panel-specific workflow is run to jointly analyze the tumor-normalmatched FASTQ files. When a matched normal is not available, FASTQ filesfrom the tumor tissue are analyzed in the ‘tumor-only’ mode. See, forexample, FIG. 5.

If two or more patient samples are processed simultaneously on the samesequencer flow cell, a difference in the sequence of the adapters usedfor each patient sample could serve the purpose of a barcode tofacilitate associating each read with the correct patient sample andplacing it in the correct FASTQ file.

For efficiency, the results of paired-end sequencing of each isolate arecontained in a split pair of FASTQ files. Forward (Read 1) and reverse(Read 2) sequences of each tumor and normal isolate are storedseparately but in the same order and under the same identifier. See, forexample, FIG. 6.

In various embodiments, the bioinformatics pipeline may filter FASTQdata from each isolate. Such filtering may include correcting or maskingsequencer errors and removing (trimming) low quality sequences or bases,adapter sequences, contaminations, chimeric reads, overrepresentedsequences, biases caused by library preparation, amplification, orcapture, and other errors (FIG. 7). Entire reads, individualnucleotides, or multiple nucleotides that are likely to have errors maybe discarded based on the quality rating associated with the read in theFASTQ file, the known error rate of the sequencer, and/or a comparisonbetween each nucleotide in the read and one or more nucleotides in otherreads that has been aligned to the same location in the referencegenome. Filtering may be done in part or in its entirety by varioussoftware tools, for example, a software tool such as Skewer (seehttps://doi.org/10.1186/1471-2105-15-182). FASTQ files may be analyzedfor rapid assessment of quality control and reads, for example, by asequencing data QC software such as AfterQC, Kraken, RNA-SeQC, FastQC,(see Illumina, BaseSpace Labs orhttps://www.illumina.com/products/by-type/informatics-products/basespace-sequence-hub/apps/fastqc.html),or another similar software program. For paired-end reads, reads may bemerged.

In a matched panel-specific tumor-normal analysis, each FASTQ file, onefor tumor, and one from normal (if available) are analyzed. In thetumor-only analysis, only tumor FASTQ is available for analysis.

Each read from the FASTQ(s) may be aligned to a location in the humangenome having a sequence that best matches the sequence of nucleotidesin the read. There are many software programs designed to align reads,for example, Novoalign (Novocraft, Inc.), Bowtie, Burrows WheelerAligner (BWA), programs that use a Smith-Waterman algorithm, etc.Alignment may be directed using a reference genome (for example, hg19,GRCh38, hg38, GRCh37, other reference genomes developed by the GenomeReference Consortium, etc.) by comparing the nucleotide sequences ineach read with portions of the nucleotide sequence in the referencegenome to determine the portion of the reference genome sequence that ismost likely to correspond to the sequence in the read. The alignment maygenerate a SAM file, which stores the locations of the start and end ofeach read according to coordinates in the reference genome and thecoverage (number of reads) for each nucleotide in the reference genome.The SAM files may be converted to BAM files, BAM files may be sorted,and duplicate reads may be marked for deletion, resulting inde-duplicated BAM files. This process produces a tumor BAM file, and anormal BAM file (when available) (e.g., as illustrated in FIG. 8). Invarious embodiments, BAM files may be analyzed to detect geneticvariants and other genetic features, including single nucleotidevariants (SNVs), copy number variants (CNVs), gene rearrangements, etc.In various aspects, the detected genetic variants and genetic featuresmay be analyzed as a form of quality control. For example, a pattern ofdetected genetic variants or features may indicate an issue related tothe sample, sequencing procedure, and/or bioinformatics pipeline, forexample, contamination of the sample, mislabeling of the sample, achange in reagents, a change in the sequencing procedure and/orbioinformatics pipeline, etc.

Calling SNVs and Indels

Following alignment, tools like SamBAMBA may be used for marking andfiltering duplicates on the sorted bams. Software packages such asfreebayes and pindel are used to call variants using the sorted BAMfiles as the input, together with genome and panel bed files containingthe gene targets to analyze as the reference. A raw VCF file (variantcall format) file is output, showing the locations where the nucleotidebase in the sample is not the same as the nucleotide base in thatposition in the reference genome. Software packages such asvcfbreakmulti and vt are used to normalize multi-nucleotide polymorphicvariants in the raw VCF file and a variant normalized VCF file isoutput. SNVs in the VCFs are annotated using SNPEff for transcriptinformation, mutation effects and prevalence in 1000 genomes databases.EGFR variants are called separately through re-alignment of tumor andnormal fastq files on chr 7 using speedseq. Duplicates are marked usingtools such as Sambamba, and variant calling is done analogous to thesteps described for other chromosomes. See, for example, FIG. 9.

Determining Copy Number Variant

In various embodiments, the systems and methods include copy numberanalysis methods to compute the genomic features used to estimate HRDstatus. For example, in some embodiments, to assess copy number,de-duplicated BAM files and a VCF generated from the variant callingpipeline may be used to compute read depth and variation in heterozygousgermline SNVs between the tumor and normal samples. If a matched normalsample is not available, comparison between a tumor sample and a pool ofprocess matched normal controls may be utilized. Circular binarysegmentation may be applied and segments may be selected with highlydifferential log 2 ratios between the tumor and its comparator (matchednormal or normal pool). Approximate integer copy number may be assessedfrom a combination of differential coverage in segmented regions and anestimate of stromal admixture (for example, tumor purity, or the portionof a sample that is tumor vs. non-tumor) generated by analysis ofheterozygous germline SNVs.

Determining Loss of Heterozygosity

In some aspects, LOH may be determined through the use of a copy numbercalling algorithm. First, the tumor purity and copy states in the tumorgenome may be estimated using an expectation maximization algorithm(EM). Estimation of copy states and tumor purity may involve thefollowing steps: 1) Read alignment and normalization 2) Computation ofB-allele frequencies and deviations 3) Preliminary estimation of tumorpurity 4) Genomic segmentation, and 5) Refinement of initial tumorpurity estimate and estimation of copy states and LOH via EM algorithm.

Read alignment and normalization. To compute probe target coverage,sequenced reads from the tumor may be aligned to the human referencegenome and normalized by length and depth and GC content. Reads from thenormal tissue may also be processed similarly, when available. If amatched normal is not available, a normal pool, consisting of readcoverages from normal healthy individuals not known to have cancer maybe used. To select a gender-matched normal pool, a gender estimationstep may be performed by mapping the variants to the X-chromosometogether with the X-chromosome coverages. From the normal pool, theclosest neighbours may be chosen, for instance through the applicationof a PCA selection step. Their coverage values may be used to normalizetumor coverages. This PCA selection increases the sensitivity of somaticCNV detection. Finally, the read coverage may be expressed as the ratioof tumor coverage to normal coverage and log 2 transformed.

Computation of B-allele frequencies and deviations. Heterozygousvariants contain useful information about copy numbers and LOH. Thesevariants may be mined from the somatic and germline variant calls madeusing freebayes and pindel. B-allele frequency (BAF) deviations from theexpected normal values are calculated for each heterozygous SNP, andalso represented as the BAF log-odds ratio. If a variant is normalgermline, the BAF deviation from normal should be close to 0. For avariant that shows LOH, BAF deviates significantly from 0.

Preliminary estimation of tumor purity. Initial estimations for tumorpurity may be obtained from somatic variants and BAF data, to be used asinput for the EM algorithm. The maximum VAF of a somatic variant shouldin theory equal the tumor purity. This is the somatic estimate of tumorpurity. From the BAF data, for a variant that shows log odds-ratiogreater than 2 is clearly LOH, as such significant deviations are onlyexpected when a copy is lost, or copy-neutral. Twice the maximumpossible VAF for such a variant should in theory equal the tumor purity,and corresponds to the BAF estimate. These two estimates are averaged toform the initial estimate of tumor purity.

Genomic segmentation. A bi-variate segmentation of the genome isperformed using tumor to normal coverage ratios and BAF log-odds data. Aseries of rolling T-tests are performed across the genome using analgorithm similar to circular binary segmentation to identify thesections of the genome where a significant switch in copy numbers isobserved. This collapses the whole genome into segments, each of whichhas a distinct copy number profile. The segmentation branching andpruning threshold parameters control how much segmentation and focalsegment detection is possible, and is optimized for Tempus data.

Refinement of initial tumor purity estimate and estimation of copystates and LOH via EM algorithm. From the initial guesses of tumorpurity, a range of tumor purity values, from half the tumor purity tomaximum possible value are iterated over to estimate the best fit copystates for each genomic segment. For each tumor purity estimate andgenomic segment, the expected log-ratio and BAF is computed for eachcopy state ranging from 0 to 20, only allowing for meaningful copy statecombinations. The likelihood of observed coverage and BAF is thencalculated given these expectations from the bivariate probabilitydensity function and a likelihood matrix is constructed. The copy statewith the maximum likelihood is returned from this matrix. This processis iterated over all segments, and a segment to best-fit copy state mapis constructed. Repeating this step for all tumor purities generates atumor-purity likelihood matrix, and the tumor purity with smallest modelerror and the maximum likelihood is returned as the final estimate. Oncethe copy state assignments are available for all genomic segments, thesegments with minor copy number of 0 are assigned LOH. These segmentsare either a 1-copy loss, copy-neutral, or a higher order LOH, dependingon the tumor purity.

Tumor Purity

To compute tumor purity, an initial tumor purity estimate was obtainedfrom somatic variants and germline B-allele frequencies, which was thenrefined using a greedy algorithm that evaluates the likelihood of thetumor purity given the tumor-normal coverage log-ratio and B-allelefrequency deviations from the normal expectation. The algorithm iteratesthrough a range of tumor-purities surrounding the initial estimate toreturn the tumor purity with the maximum likelihood.

Loss of Heterozygosity

For estimation of genome-wide loss of heterozygosity (LOH), each SNP wasevaluated for LOH based on the germline variant allele fraction anddeviation of B-allele frequencies from normal expectation. A binary 0/1system was used to assign no LOH/LOH and average proportion of genomicbases under LOH was obtained. The number of bases undergoing LOH may bedivided by the total number of bases analyzed using a copy numbermethod, such as the method described in this patent, to determine agenome-wide LOH proportion estimate. In one example, the genome-wide LOHproportion estimate may represent LOH in the somatic (cancer) samplethat may not be present in the germline (normal) sample.

Average LOH at BRCA1 and BRCA2 genes may be determined in a likewisemanner, but considering only the two gene coordinates. In one example,the LOH for BRCA 1/2 genes may represent LOH in the somatic (cancer)sample that may not be present in the germline (normal) sample.

Counting Pathogenic Variant Counts

For counting pathogenic variant counts in specific genes, we used allthe SNPs called for each patient, and matched them up with a curatedreference mutation list that contains a list of known pathogenic andtruncating BRCA variants, e.g., BRCA1 and BRCA2. A pathogenic variantcount was then obtained based on the overlap in SNP positions. Aseparate somatic and germline variant count is also output for BRCA. Asum of the two counts may also be generated.

In some embodiments, the pathogenic variants used in the systems andmethods described herein include one or more of the variants listed inTable 1. In some embodiments, the pathogenic variants used in thesystems and methods described herein include at least 5, 10, 15, 20, 25,30, 40, 50, 75, 100, 125, or all of the variants listed in Table 1.

TABLE 1 Examples of pathogenic BRCA1 and BRCA2 variants ReferenceAlternate Chr Position allele allele cpra 13 32890558 G A13_32890558_G_A 13 32890593 TA T 13_32890593_TA_T 13 32893214 A C13_32893214_A_C 13 32893239 G A 13_32893239_G_A 13 32893373 C A13_32893373_C_A 13 32893435 G T 13_32893435_G_T 13 32900288 G T13_32900288_G_T 13 32900751 G A 13_32900751_G_A 13 32903604 CTG C13_32903604_CTG_C 13 32905165 AT A 13_32905165_AT_A 13 32906571ATCTACAAAAAG A 13_32906571_ATCTACAAAAAG_A 13 32906909 G T13_32906909_G_T 13 32907331 AGCTTT A 13_32907331_AGCTTT_A 13 32907365AAAAAG A 13_32907365_AAAAAG_A 13 32907408 CATCTT C 13_32907408_CATCTT_C13 32907420 G GA 13_32907420_G_GA 13 32907420 GA G 13_32907420_GA_G 1332910644 G T 13_32910644_G_T 13 32910797 C CT 13_32910797_C_CT 1332911001 GA G 13_32911001_GA_G 13 32911297 TAAAC T 13_32911297_TAAAC_T13 32911321 T TA 13_32911321_T_TA 13 32911356 AC A 13_32911356_AC_A 1332911380 T TA 13_32911380_T_TA 13 32911470 G A 13_32911470_G_A 1332911757 C T 13_32911757_C_T 13 32911775 C T 13_32911775_C_T 13 32911877C T 13_32911877_C_T 13 32911968 GC G 13_32911968_GC_G 13 32912089 CTG C13_32912089_CTG_C 13 32912171 CTG C 13_32912171_CTG_C 13 32912351 AATAATA 13_32912351_AATAAT_A 13 32912398 TG T 13_32912398_TG_T 13 32912503 TGT 13_32912503_TG_T 13 32912539 TCATA T 13_32912539_TCATA_T 13 32912587 TA 13_32912587_T_A 13 32912701 TTCAAA T 13_32912701_TTCAAA_T 13 32912703C A 13_32912703_C_A 13 32912735 G T 13_32912735_G_T 13 32912770 A AT13_32912770_A_AT 13 32912967 AAG A 13_32912967_AAG_A 13 32913118 GA G13_32913118_GA_G 13 32913139 AG A 13_32913139_AG_A 13 32913143 C T13_32913143_C_T 13 32913181 G A 13_32913181_G_A 13 32913457 C G13_32913457_C_G 13 32913558 C CA 13_32913558_C_CA 13 32913648 A AT13_32913648_A_AT 13 32913668 G GA 13_32913668_G_GA 13 32913708 ATTTAAGTA 13_32913708_ATTTAAGT_A 13 32913836 CA C 13_32913836_CA_C 13 32914014CA C 13_32914014_CA_C 13 32914102 CAGTAA C 13_32914102_CAGTAA_C 1332914191 C G 13_32914191_C_G 13 32914209 ACT A 13_32914209_ACT_A 1332914226 G T 13_32914226_G_T 13 32914247 A T 13_32914247_A_T 13 32914250GT G 13_32914250_GT_G 13 32914349 G T 13_32914349_G_T 13 32914437 GT G13_32914437_GT_G 13 32914502 G T 13_32914502_G_T 13 32914715 A T13_32914715_A_T 13 32914757 G T 13_32914757_G_T 13 32914766 CTT C13_32914766_CTT_C 13 32914851 C A 13_32914851_C_A 13 32915135 TACTC T13_32915135_TACTC_T 13 32915292 C G 13_32915292_C_G 13 32920978 C T13_32920978_C_T 13 32929238 TCA T 13_32929238_TCA_T 13 32929275 G T13_32929275_G_T 13 32929426 G A 13_32929426_G_A 13 32931878 G A13_32931878_G_A 13 32936711 G A 13_32936711_G_A 13 32936732 G C13_32936732_G_C 13 32936732 G T 13_32936732_G_T 13 32936828 C A13_32936828_C_A 13 32936830 G A 13_32936830_G_A 13 32937354 T TA13_32937354_T_TA 13 32937354 TA T 13_32937354_TA_T 13 32937479 CA C13_32937479_CA_C 13 32937506 G C 13_32937506_G_C 13 32944693 A G13_32944693_A_G 13 32950809 AAC A 13_32950809_AAC_A 13 32950889 T G13_32950889_T_G 13 32950928 G A 13_32950928_G_A 13 32950932 A G13_32950932_A_G 13 32953453 G A 13_32953453_G_A 13 32953526 C T13_32953526_C_T 13 32953556 G T 13_32953556_G_T 13 32953640 G GA13_32953640_G_GA 13 32953886 G A 13_32953886_G_A 13 32953974 C G13_32953974_C_G 13 32954022 C CA 13_32954022_C_CA 13 32954022 CA C13_32954022_CA_C 13 32954050 G A 13_32954050_G_A 13 32954147 TC T13_32954147_TC_T 13 32954180 C T 13_32954180_C_T 13 32954222 C T13_32954222_C_T 13 32954272 G GA 13_32954272_G_GA 13 32954272 GA G13_32954272_GA_G 13 32968850 C A 13_32968850_C_A 13 32968863 C G13_32968863_C_G 17 41197784 G A 17_41197784_G_A 17 41199658 A T17_41199658_A_T 17 41203122 G GC 17_41203122_G_GC 17 41209079 T TG17_41209079_T_TG 17 41209154 T C 17_41209154_T_C 17 41215362 TTTTC T17_41215362_TTTTC_T 17 41215948 G A 17_41215948_G_A 17 41223097 G A17_41223097_G_A 17 41223176 TG T 17_41223176_TG_T 17 41223242 G C17_41223242_G_C 17 41226411 G A 17_41226411_G_A 17 41226447 CTT C17_41226447_CTT_C 17 41234421 CT C 17_41234421_CT_C 17 41234451 G A17_41234451_G_A 17 41243479 CTTGA C 17_41243479_CTTGA_C 17 41243533 C A17_41243533_C_A 17 41243704 C A 17_41243704_C_A 17 41243800 C A17_41243800_C_A 17 41243843 GTTTAC G 17_41243843_GTTTAC_G 17 41244281 CAC 17_41244281_CA_C 17 41244614 A C 17_41244614_A_C 17 41244865 GTT G17_41244865_GTT_G 17 41244913 C A 17_41244913_C_A 17 41245161 G GT17_41245161_G_GT 17 41245203 CT C 17_41245203_CT_C 17 41245330 CTT C17_41245330_CTT_C 17 41245513 T A 17_41245513_T_A 17 41245586 CT C17_41245586_CT_C 17 41245603 C A 17_41245603_C_A 17 41245834 C A17_41245834_C_A 17 41245888 C A 17_41245888_C_A 17 41246186 ACT A17_41246186_ACT_A 17 41246197 AT A 17_41246197_AT_A 17 41246278 CAG C17_41246278_CAG_C 17 41246531 CT C 17_41246531_CT_C 17 41246539 C A17_41246539_C_A 17 41246723 G GCCACATGGCT 17_41246723_G_GCCACATGGCT 1741247864 C CT 17_41247864_C_CT 17 41247940 C A 17_41247940_CA 1741256203 TG T 17_41256203_TG_T 17 41258504 A C 17_41258504_A_C 1741267762 A G 17_41267762_A_G 17 41276044 ACT A 17_41276044_ACT_A 1741276048 TAA T 17_41276048_TAA_T

Positive HRD Calls Based on HRD Markers

In various aspects, if certain markers of HRD are detected, the systemsand methods disclosed herein return a positive HRD call. In one example,if a pathogenic stop gain or frameshift variant is present in BRCA1 orBRCA2 a positive HRD call is returned. In another example, ifgenome-wide loss of heterozygosity proportion is above the thresholdindicative of BRCA mutation, combined with loss of heterozygosity ofBRCA1 or BRCA2, a positive HRD call is returned.

Classifiers

Generally, many different classification algorithms find use in thesystems and methods described herein. For instance, in some embodiments,the model is a neural network algorithm, a support vector machinealgorithm, a Naive Bayes algorithm, a nearest neighbor algorithm, aboosted trees algorithm, a random forest algorithm, a decision treealgorithm, a multinomial logistic regression algorithm, a linear model,or a linear regression algorithm.

In some embodiments, the classification algorithm used in the systemsand methods described herein is a random forest algorithm. In someembodiments, the trained classification method comprises a trainedclassifier stream. In some embodiments, by way of non-limiting examplethe trained classifier stream is a decision tree. Decision treealgorithms suitable for use as the classification models describedherein are described in, for example, Duda, 2001, PatternClassification, John Wiley & Sons, Inc., New York, 395-396, which ishereby incorporated by reference. Tree-based methods partition thefeature space into a set of rectangles, and then fit a model (like aconstant) in each one. In some embodiments, the decision tree is randomforest regression. One specific algorithm that can be used as theclassification model is a classification and regression tree (CART).Other examples of specific decision tree algorithms that can be used asthe classifier include, but are not limited to, ID3, C4.5, MART, andRandom Forests. CART, ID3, and C4.5 are described in Duda, 2001, PatternClassification, John Wiley & Sons, Inc., New York. 396-408 and 411-412,which is hereby incorporated by reference. CART, MART, and C4.5 aredescribed in Hastie et al., 2001, The Elements of Statistical Learning,Springer-Verlag, New York, Chapter 9, which is hereby incorporated byreference in its entirety. Random Forests are described in Breiman,1999, “Random Forests—Random Features,” Technical Report 567, StatisticsDepartment, U.C. Berkeley, September 1999, which is hereby incorporatedby reference in its entirety.

In some embodiments, tumor organoids with varied BRCA LOH statuses,pathogenic mutations and genome-wide LOH measurements may be grown andtreated with PARP inhibitors to obtain an in-vitro PARP drug response.Samples could span a wide range of cancer cohorts. Tumor cell linesexpected to be PARP sensitive may be tested alongside negative controlsthat have no HRD mutations. The PARP outcome data may be used to refineinput features in the random forest classifier. Additional informationcould be gleaned from mutational signatures and other genes in the HRDpathway. See, for example, Gulhan D C, Lee J J, Melloni G E M,Cortés-Ciriano I, Park P J, “Detecting the mutational signature ofhomologous recombination deficiency in clinical samples,” Nat Genet.,51(5):912-19 (2019), which is incorporated by reference herein.

In an alternative embodiment, instead of or in addition to training arandom forest classifier to generate HRD calls, the systems and methodsuse business logic. For example, in some embodiments, a business ruleset, such as is illustrated in FIG. 10, is used in the systems andmethods described herein.

In some embodiments, the classification algorithm used the systems andmethods described herein is a regression algorithm. The regressionalgorithm can be any type of regression. For example, in someembodiments, the regression algorithm is logistic regression. Logisticregression algorithms are disclosed in Agresti, An Introduction toCategorical Data Analysis, 1996, Chapter 5, pp. 103-144, John Wiley &Son, New York, which is hereby incorporated by reference. In someembodiments, the regression algorithm is logistic regression with lasso,L2 or elastic net regularization.

In some embodiments, the classification algorithm used the systems andmethods described herein is a neural network. Examples of neural networkalgorithms, including convolutional neural network algorithms, aredisclosed, for example, in Vincent et al., 2010, “Stacked denoisingautoencoders: Learning useful representations in a deep network with alocal denoising criterion,” J Mach Learn Res 11, pp. 3371-3408;Larochelle et al., 2009, “Exploring strategies for training deep neuralnetworks,” J Mach Learn Res 10, pp. 1-40; and Hassoun, 1995,Fundamentals of Artificial Neural Networks, Massachusetts Institute ofTechnology, each of which is hereby incorporated by reference.

In some embodiments, the classification algorithm used the systems andmethods described herein is a support vector machine (SVM). Examples ofSVM algorithms are described, for example, in Cristianini andShawe-Taylor, 2000, “An Introduction to Support Vector Machines,”Cambridge University Press, Cambridge; Boser et al., 1992, “A trainingalgorithm for optimal margin classifiers,” in Proceedings of the 5thAnnual ACM Workshop on Computational Learning Theory, ACM Press,Pittsburgh, Pa., pp. 142-152; Vapnik, 1998, Statistical Learning Theory,Wiley, New York; Mount, 2001, Bioinformatics: sequence and genomeanalysis, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y.;Duda, Pattern Classification, Second Edition, 2001, John Wiley & Sons,Inc., pp. 259, 262-265; and Hastie, 2001, The Elements of StatisticalLearning, Springer, New York; and Furey et al., 2000, Bioinformatics 16,906-914, each of which is hereby incorporated by reference in itsentirety. When used for classification, SVMs separate a given set ofbinary labeled data training set with a hyper-plane that is maximallydistant from the labeled data. For cases in which no linear separationis possible, SVMs can work in combination with the technique of“kernels,” which automatically realizes a non-linear mapping to afeature space. The hyper-plane found by the SVM in feature spacecorresponds to a non-linear decision boundary in the input space.

In some embodiments, the machine-learned model includes a logisticregression classifier. In other embodiments, the machine learning ordeep learning model can be one of a decision tree, an ensemble (e.g.,bagging, boosting, random forest), gradient boosting machine, linearregression, Naïve Bayes, or a neural network. The HRD model includeslearned weights for the features that are adjusted during training. Theterm “weights” is used generically here to represent the learnedquantity associated with any given feature of a model, regardless ofwhich particular machine learning technique is used. In someembodiments, a cancer indicator score is determined by inputting valuesfor features derived from one or more DNA sequences (or DNA sequencereads thereof) into a machine learning or deep learning model.

In some embodiments, e.g., when the HRD evaluation model is a neuralnetwork (e.g., a conventional or convolutional neural network), theoutput of a disease classifier is a classification, e.g., either cancerpositive or cancer negative. However, in some embodiments, in order toprovide a continuous or semi-continuous value for the output of themodel, rather than a classification, a hidden layer of a neural network,e.g., the hidden layer just prior to the output layer, is used as theoutput of the classification model.

Accordingly, in some embodiments, the model includes (i) an input layerfor receiving values for the plurality of genotypic characteristics,where the plurality of genotypic characteristics includes a first numberof dimensions, and (ii) an embedding layer that includes a set ofweights, where the embedding layer directly or indirectly receivesoutput of the input layer, and where an output of the embedding layer isa model score set having a second number of dimensions that is less thanthe first number of dimensions, and (iii) an output layer that directlyor indirectly receives the model score set from the embedding layer. Insome embodiments, the output of the classifier is an output of a set ofneurons associated with a hidden layer in a neural network termed theembedding layer. In such embodiments, each such neuron in the embeddinglayer is associated with a weight and an activation function and theoutput consists of the output of each such activation function. In someembodiments, the activation function of a neuron in the embedding layeris rectified linear unit (ReLU), tanh, or sigmoid activation function.In some such embodiments, the neurons of the embedding layer are fullyconnected to each of the inputs of the input layer. In some suchembodiments, each neuron of the output layer is fully connected to eachneuron of the embedding layer. In some embodiments, each neuron of theoutput layer is associated with a Softmax activation function. In someembodiments, one or more of the embedding layer and the output layer isnot fully connected.

Patient Report

In some embodiments, a patient report is generated based on the outputof the classifier. The report may be presented to a patient, physician,medical personnel, or researcher in a digital copy (for example, a JSONobject, pdf file or an image on a website or portal), a hard copy (forexample, printed on paper or another tangible medium), or in anotherformat.

In some embodiments, the report includes information related to the HRDstatus of the specimen, detected genetic variants, other characteristicsof a patient's sample, and/or clinical records. The report may furtherinclude clinical trials for which the patient is eligible, therapiesthat may match the patient and/or adverse effects predicted if thepatient receives a given therapy, based on the HRD status, detectedgenetic variants, other characteristics of the sample and/or clinicalrecords. In one example, if a patient specimen is predicted to have HRD,the patient may be matched with PARP inhibitors, platinum-basedchemotherapy, and/or additional DNA-damaging therapies.

The results included in the report and/or additional results (forexample, from the bioinformatics pipeline) may be used to analyze adatabase of clinical data, especially to determine whether there is atrend showing that a therapy slowed cancer progression in other patientshaving the same or similar results as the specimen. The results may alsobe used to design tumor organoid experiments. For example, an organoidmay be genetically engineered to have the same characteristics as thespecimen and may be observed after exposure to a therapy to determinewhether the therapy can reduce the growth rate of the organoid, and thusis likely to reduce the growth rate of the patient associated with thespecimen.

In this example, HRD information may be stored in a report object, suchas a JSON object, for further processing and/or display. For example,information from the report object may be used to prepare a clinicallaboratory report for return to an ordering physician. The informationmay be provided as a combination of text, images, and/or audio. Anexample display of text and images that indicate HRD information ispresented as FIG. 11.

In some embodiments, the report also includes a listing of geneticvariants related to the genes in the homologous recombination DNA repairpathway and/or genes that interact with this pathway. An example displayfor this listing is presented as FIG. 12.

Therapy

In some aspects, the systems and methods disclosed herein may be used asa companion diagnostic. For example, in some embodiments, an estimatedHRD status may be used by a clinician to make a decision to treat acancer with a PARP inhibitor.

Table 2 lists several PARP inhibitors and the FDA approval or clinicaltrial status of each PARP inhibitor for various cancer types in 2019.This table illustrates the widespread potential utility of PARPinhibitors for patients who have tested positive for HRD.

TABLE 2 Example PARP inhibitors FDA Drug Cancer Types Approval OlaparibOvarian, Breast Approved Gastric, Gastroesophageal Junction, Prostate,Trial Lung (SC/NSC), Pancreatic Fallopian, Primary Peritoneal,Urothelial (Bladder), Pediatric Solid Tumors & Non-Hodgkin's RucaparibOvarian Approved Fallopian, Primary Peritoneal, Any BRCA1/2 Trial Solid,Urothelial, Prostate, Endometrial Niraparib Ovarian, Fallopian, PrimaryPeritoneal Approved Pancreatic, Prostate, Solid Trial Talazoparib BreastApproved Advanced or Recurrent Solid Tumors, Breast Trial Neoplasms,Epithelial Ovarian Cancer, Ewing Sarcoma, Small Cell Lung Carcinoma,Prostate Cancer, Pancreas Cancer

In some aspects, an estimated HRD status may be used by a clinician tomake a decision to treat a cancer with the addition of platinum tostandard neoadjuvant chemotherapy. Adding a platinum agent to standardcombination chemotherapy increases the toxicity of treatment, and sopatients will benefit from an estimated HRD that indicates whether theircancer is more likely to be treated through the combination of aplatinum agent and standard combination chemotherapy.

In some aspects, PARP inhibitors have been approved for treatment ofcancers harboring specifically germline alterations. For example,olaparib is approved for germline BRCA (gBRCA) positive ovarian cancertreated with at least 3 prior chemo regimens and talozaparib is approvedfor gBRCA positive, HER2 negative localized or metastatic breast cancer.Detecting germline variants in BRCA or other genes related to DNA repairpathways may aid a physician in deciding to prescribe PARPi.

Implementation Using a Digital and Laboratory Health Care Platform

The methods and systems described herein may be utilized in combinationwith or as part of a digital and laboratory health care platform that isgenerally targeted to medical care and research. It should be understoodthat many uses of the methods and systems described above, incombination with such a platform, are possible. One example of such aplatform is described in U.S. patent application Ser. No. 16/657,804,titled “Data Based Cancer Research and Treatment Systems and Methods”,and filed Oct. 18, 2019, which is incorporated herein by reference andin its entirety for all purposes.

For example, an implementation of one or more embodiments of the methodsand systems as described above may include microservices constituting adigital and laboratory health care platform supporting HRD detection.Embodiments may include a single microservice for executing anddelivering ______ or may include a plurality of microservices eachhaving a particular role which together implement one or more of theembodiments above. In one example, a first microservice may executecomputation of genomic features in order to deliver features to a secondmicroservice for training an HRD model. Similarly, the secondmicroservice may execute training an HRD model to deliver a trained HRDmodel to a third microservice according to an embodiment, above. A thirdmicroservice may use a trained HRD model to analyze data associated witha specimen to determine the likelihood of the specimen having HRD.

Where embodiments above are executed in one or more micro-services withor as part of a digital and laboratory health care platform, one or moreof such micro-services may be part of an order management system thatorchestrates the sequence of events as needed at the appropriate timeand in the appropriate order necessary to instantiate embodiments above.A micro-services based order management system is disclosed, forexample, in U.S. Prov. Patent Application No. 62/873,693, titled“Adaptive Order Fulfillment and Tracking Methods and Systems”, filedJul. 12, 2019, which is incorporated herein by reference and in itsentirety for all purposes.

For example, continuing with the above first and second microservices,an order management system may notify the first microservice that anorder for ______ has been received and is ready for processing. Thefirst microservice may execute and notify the order management systemonce the delivery of ______ is ready for the second microservice.Furthermore, the order management system may identify that executionparameters (prerequisites) for the second microservice are satisfied,including that the first microservice has completed, and notify thesecond microservice that it may continue processing the order to ______according to an embodiment, above.

Where the digital and laboratory health care platform further includes agenetic analyzer system, the genetic analyzer system may includetargeted panels and/or sequencing probes. An example of a targeted panelis disclosed, for example, in U.S. Prov. Patent Application No.62/902,950, titled “System and Method for Expanding Clinical Options forCancer Patients using Integrated Genomic Profiling”, and filed Sep. 19,2019, which is incorporated herein by reference and in its entirety forall purposes. In one example, targeted panels may enable the delivery ofnext generation sequencing results for ______ according to anembodiment, above. An example of the design of next-generationsequencing probes is disclosed, for example, in U.S. Prov. PatentApplication No. 62/924,073, titled “Systems and Methods for NextGeneration Sequencing Uniform Probe Design”, and filed Oct. 21, 2019,which is incorporated herein by reference and in its entirety for allpurposes.

Where the digital and laboratory health care platform further includes abioinformatics pipeline, the methods and systems described above may beutilized after completion or substantial completion of the systems andmethods utilized in the bioinformatics pipeline. As one example, thebioinformatics pipeline may receive next-generation genetic sequencingresults and return a set of binary files, such as one or more BAM files,reflecting DNA and/or RNA read counts aligned to a reference genome. Themethods and systems described above may be utilized, for example, toingest the DNA and/or RNA read counts and produce ______ as a result.

When the digital and laboratory health care platform further includes anRNA data normalizer, any RNA read counts may be normalized beforeprocessing embodiments as described above. An example of an RNA datanormalizer is disclosed, for example, in U.S. patent application Ser.No. 16/581,706, titled “Methods of Normalizing and Correcting RNAExpression Data”, and filed Sep. 24, 2019, which is incorporated hereinby reference and in its entirety for all purposes.

When the digital and laboratory health care platform further includes agenetic data deconvoluter, any system and method for deconvoluting maybe utilized for analyzing genetic data associated with a specimen havingtwo or more biological components to determine the contribution of eachcomponent to the genetic data and/or determine what genetic data wouldbe associated with any component of the specimen if it were purified. Anexample of a genetic data deconvoluter is disclosed, for example, inU.S. patent application Ser. No. 16/732,229 and PCT19/69161, both titled“Transcriptome Deconvolution of Metastatic Tissue Samples”, and filedDec. 31, 2019, U.S. Prov. Patent Application No. 62/924,054, titled“Calculating Cell-type RNA Profiles for Diagnosis and Treatment”, andfiled Oct. 21, 2019, and U.S. Prov. Patent Application No. 62/944,995,titled “Rapid Deconvolution of Bulk RNA Transcriptomes for Large DataSets (Including Transcriptomes of Specimens Having Two or More TissueTypes)”, and filed Dec. 6, 2019 which are incorporated herein byreference and in their entirety for all purposes.

When the digital and laboratory health care platform further includes anautomated RNA expression caller, RNA expression levels may be adjustedto be expressed as a value relative to a reference expression level,which is often done in order to prepare multiple RNA expression datasets for analysis to avoid artifacts caused when the data sets havedifferences because they have not been generated by using the samemethods, equipment, and/or reagents. An example of an automated RNAexpression caller is disclosed, for example, in U.S. Prov. PatentApplication No. 62/943,712, titled “Systems and Methods for AutomatingRNA Expression Calls in a Cancer Prediction Pipeline”, and filed Dec. 4,2019, which is incorporated herein by reference and in its entirety forall purposes.

The digital and laboratory health care platform may further include oneor more insight engines to deliver information, characteristics, ordeterminations related to a disease state that may be based on geneticand/or clinical data associated with a patient and/or specimen.Exemplary insight engines may include a tumor of unknown origin engine,a human leukocyte antigen (HLA) loss of homozygosity (LOH) engine, atumor mutational burden engine, a PD-L1 status engine, a homologousrecombination deficiency engine, a cellular pathway activation reportengine, an immune infiltration engine, a microsatellite instabilityengine, a pathogen infection status engine, and so forth. An exampletumor of unknown origin engine is disclosed, for example, in U.S. Prov.Patent Application No. 62/855,750, titled “Systems and Methods forMulti-Label Cancer Classification”, and filed May 31, 2019, which isincorporated herein by reference and in its entirety for all purposes.An example of an HLA LOH engine is disclosed, for example, in U.S. Prov.Patent Application No. 62/889,510, titled “Detection of Human LeukocyteAntigen Loss of Heterozygosity”, and filed Aug. 20, 2019, which isincorporated herein by reference and in its entirety for all purposes.An example of a tumor mutational burden (TMB) engine is disclosed, forexample, in U.S. Prov. Patent Application No. 62/804,458, titled“Assessment of Tumor Burden Methodologies for Targeted PanelSequencing”, and filed Feb. 12, 2019, which is incorporated herein byreference and in its entirety for all purposes. An example of a PD-L1status engine is disclosed, for example, in U.S. Prov. PatentApplication No. 62/854,400, titled “A Pan-Cancer Model to Predict ThePD-L1 Status of a Cancer Cell Sample Using RNA Expression Data and OtherPatient Data”, and filed May 30, 2019, which is incorporated herein byreference and in its entirety for all purposes. An additional example ofa PD-L1 status engine is disclosed, for example, in U.S. Prov. PatentApplication No. 62/824,039, titled “PD-L1 Prediction Using H&E SlideImages”, and filed Mar. 26, 2019, which is incorporated herein byreference and in its entirety for all purposes. The systems and methodsdisclosed herein are an example of a homologous recombination deficiencyengine. An alternative homologous recombination deficiency engine isdisclosed, for example, in U.S. Prov. Patent Application No. 62/804,730,titled “An Integrative Machine-Learning Framework to Predict HomologousRecombination Deficiency”, and filed Feb. 12, 2019, which isincorporated herein by reference and in its entirety for all purposes.An example of a cellular pathway activation report engine is disclosed,for example, in U.S. Prov. Patent Application No. 62/888,163, titled“Cellular Pathway Report”, and filed Aug. 16, 2019, which isincorporated herein by reference and in its entirety for all purposes.An example of an immune infiltration engine is disclosed, for example,in U.S. patent application Ser. No. 16/533,676, titled “A Multi-ModalApproach to Predicting Immune Infiltration Based on Integrated RNAExpression and Imaging Features”, and filed Aug. 6, 2019, which isincorporated herein by reference and in its entirety for all purposes.An additional example of an immune infiltration engine is disclosed, forexample, in U.S. Patent Application No. 62/804,509, titled“Comprehensive Evaluation of RNA Immune System for the Identification ofPatients with an Immunologically Active Tumor Microenvironment”, andfiled Feb. 12, 2019, which is incorporated herein by reference and inits entirety for all purposes. An example of an MSI engine is disclosed,for example, in U.S. patent application Ser. No. 16/653,868, titled“Microsatellite Instability Determination System and Related Methods”,and filed Oct. 15, 2019, which is incorporated herein by reference andin its entirety for all purposes. An additional example of an MSI engineis disclosed, for example, in U.S. Prov. Patent Application No.62/931,600, titled “Systems and Methods for Detecting MicrosatelliteInstability of a Cancer Using a Liquid Biopsy”, and filed Nov. 6, 2019,which is incorporated herein by reference and in its entirety for allpurposes.

When the digital and laboratory health care platform further includes areport generation engine, the methods and systems described above may beutilized to create a summary report of a patient's genetic profile andthe results of one or more insight engines for presentation to aphysician. For instance, the report may provide to the physicianinformation about the extent to which the specimen that was sequencedcontained tumor or normal tissue from a first organ, a second organ, athird organ, and so forth. For example, the report may provide a geneticprofile for each of the tissue types, tumors, or organs in the specimen.The genetic profile may represent genetic sequences present in thetissue type, tumor, or organ and may include variants, expressionlevels, information about gene products, or other information that couldbe derived from genetic analysis of a tissue, tumor, or organ. Thereport may include therapies and/or clinical trials matched based on aportion or all of the genetic profile or insight engine findings andsummaries. For example, the therapies may be matched according to thesystems and methods disclosed in U.S. Prov. Patent Application No.62/804,724, titled “Therapeutic Suggestion Improvements Gained ThroughGenomic Biomarker Matching Plus Clinical History”, filed Feb. 12, 2019,which is incorporated herein by reference and in its entirety for allpurposes. For example, the clinical trials may be matched according tothe systems and methods disclosed in U.S. Prov. Patent Application No.62/855,913, titled “Systems and Methods of Clinical Trial Evaluation”,filed May 31, 2019, which is incorporated herein by reference and in itsentirety for all purposes.

The report may include a comparison of the results to a database ofresults from many specimens. An example of methods and systems forcomparing results to a database of results are disclosed in U.S. Prov.Patent Application No. 62/786,739, titled “A Method and Process forPredicting and Analyzing Patient Cohort Response, Progression andSurvival”, and filed Dec. 31, 2018, which is incorporated herein byreference and in its entirety for all purposes. The information may beused, sometimes in conjunction with similar information from additionalspecimens and/or clinical response information, to discover biomarkersor design a clinical trial.

When the digital and laboratory health care platform further includesapplication of one or more of the embodiments herein to organoidsdeveloped in connection with the platform, the methods and systems maybe used to further evaluate genetic sequencing data derived from anorganoid to provide information about the extent to which the organoidthat was sequenced contained a first cell type, a second cell type, athird cell type, and so forth. For example, the report may provide agenetic profile for each of the cell types in the specimen. The geneticprofile may represent genetic sequences present in a given cell type andmay include variants, expression levels, information about geneproducts, or other information that could be derived from geneticanalysis of a cell. The report may include therapies matched based on aportion or all of the deconvoluted information. These therapies may betested on the organoid, derivatives of that organoid, and/or similarorganoids to determine an organoid's sensitivity to those therapies. Forexample, organoids may be cultured and tested according to the systemsand methods disclosed in U.S. patent application Ser. No. 16/693,117,titled “Tumor Organoid Culture Compositions, Systems, and Methods”,filed Nov. 22, 2019; U.S. Prov. Patent Application No. 62/924,621,titled “Systems and Methods for Predicting Therapeutic Sensitivity”,filed Oct. 22, 2019; and U.S. Prov. Patent Application No. 62/944,292,titled “Large Scale Phenotypic Organoid Analysis”, filed Dec. 5, 2019,which are incorporated herein by reference and in their entirety for allpurposes.

When the digital and laboratory health care platform further includesapplication of one or more of the above in combination with or as partof a medical device or a laboratory developed test that is generallytargeted to medical care and research, such laboratory developed test ormedical device results may be enhanced and personalized through the useof artificial intelligence. An example of laboratory developed tests,especially those that may be enhanced by artificial intelligence, isdisclosed, for example, in U.S. Provisional Patent Application No.62/924,515, titled “Artificial Intelligence Assisted Precision MedicineEnhancements to Standardized Laboratory Diagnostic Testing”, and filedOct. 22, 2019, which is incorporated herein by reference and in itsentirety for all purposes.

It should be understood that the examples given above are illustrativeand do not limit the uses of the systems and methods described herein incombination with a digital and laboratory health care platform.

EXAMPLES Example 1—Analysis of an Initial HRD Prediction Model

The accuracy of an initial HRD prediction algorithm, as describedherein, was evaluated using a small 40 sample training set curated withsamples having known pathogenic mutations in BRCA. All the genomicfeatures needed for HRD prediction were computed on the training samplesusing CONA. The sklearn ‘train_test_split’ method was used to createtraining and test sets for initial validation. The sklearn‘standard_scaler’ and ‘fit_transform’ method was used to normalize themean and variance in training samples and also to keep the future testdata scale identical. The ‘RandomForestClassifier’ method was used tocreate a random forest classifier with the number of genomic featuresset as ‘n_estimators’. Using the ‘compute_simple_cross_val_score’ wecomputed a simple 5-fold cross validation score metric and obtained a99% classification accuracy. Top k-features were obtained using thestandard Gini criterion. We used pickle to dump the classification modelto a file, and loaded the model to make predictions for each testsample. For each patient, we first computed the HRD features using CONA,and standardized the features using the same scaling function used forthe training samples. The probability of HRD was then obtained giventhese standardized features using the ‘model.predict_proba’ functionimplemented in sklearn. The confidence in HRD prediction is the modelprediction probability, and a positive call is defined for samples withprobability >0.5. Any new features can easily be incorporated into thismodel, and the training set can be easily expanded for re-training andprediction.

Example 2—Analysis of an Initial HRD Prediction Model

The HRD status of 1000 patient samples across 35 different cancer-typeswere analyzed using an HRD classifier as described herein. The analysisidentified a total 6.4% HRD-positive calls. Pathogenic variants in BRCAgenes were significantly greater in HRD-positive calls than negativecalls (P<4.1e-219, Mann-Whitney test), while LOH in BRCA was notenriched (P<0.06, Mann-Whitney test). Ovarian cancer (12% HRD-positive,n=57), breast cancer (14.6%, n=89), and colorectal cancer (10%, n=285)were some of the most-represented cancer types. Contrary topreviously-published results, few patients with pancreatic (2.3%, n=295)and prostate (2.7%, n=37) had predicted HRD.

REFERENCES CITED AND ALTERNATIVE EMBODIMENTS

All references cited herein are incorporated herein by reference intheir entirety and for all purposes to the same extent as if eachindividual publication or patent or patent application was specificallyand individually indicated to be incorporated by reference in itsentirety for all purposes.

The present invention can be implemented as a computer program productthat comprises a computer program mechanism embedded in a non-transitorycomputer readable storage medium. For instance, the computer programproduct could contain the program modules shown in any combination inFIG. 1 and/or as described elsewhere within the application. Theseprogram modules can be stored on a CD-ROM, DVD, magnetic disk storageproduct, USB key, or any other non-transitory computer readable data orprogram storage product.

Many modifications and variations of this disclosure can be made withoutdeparting from its spirit and scope, as will be apparent to thoseskilled in the art. The specific embodiments described herein areoffered by way of example only. The embodiments were chosen anddescribed in order to best explain the principles of the invention andits practical applications, to thereby enable others skilled in the artto best utilize the invention and various embodiments with variousmodifications as are suited to the particular use contemplated. Thedisclosure is to be limited only by the terms of the appended claims,along with the full scope of equivalents to which such claims areentitled.

What is claimed is:
 1. A method of determining a homologousrecombination pathway status of a cancer in a test subject, the methodcomprising: at a computer system having one or more processors, andmemory storing one or more programs for execution by the one or moreprocessors: (A) obtaining a first plurality of sequence reads, inelectronic form, of a first DNA sample from the test subject, the firstDNA sample comprising DNA molecules from a cancerous tissue of thesubject; (B) obtaining a second plurality of sequence reads, inelectronic, of a second DNA sample from the test subject, the second DNAsample consisting of DNA molecules from a non-cancerous tissue of thesubject; (C) generating, based on the first plurality of sequence readsand the second plurality of sequence reads, a genomic data construct forthe subject, the genomic data construct comprising one or more afeatures of the genomes of the cancerous and non-cancerous tissues ofthe subject, the plurality of features including (i) a heterozygositystatus for a first plurality of DNA damage repair genes in the genome ofthe cancerous tissue of the subject, (ii) a measure of the loss ofheterozygosity across the genome of the cancerous tissue of the subject,(iii) a measure of variant alleles detected in a second plurality of DNAdamage repair genes in the genome of the cancerous tissue of thesubject, and (iv) a measure of variant alleles detected in the secondplurality of DNA damage repair genes in the genome of the non-canceroustissue of the subject; and (D) inputting the genomic data construct intoa classifier trained to distinguish between cancers with homologousrecombination pathway deficiencies and cancers without homologousrecombination pathway deficiencies, thereby determining the homologousrecombination pathway status of the test subject.
 2. The method of claim1, wherein the first DNA sample is from a solid tumor biopsy of thecancerous tissue of the subject.
 3. The method of claim 1, wherein thesecond DNA sample is from a buffy coat preparation of a blood samplefrom the subject.
 4. The method of claim 1, wherein the first pluralityof sequence reads was generated by targeted sequencing using a pluralityof nucleic acid probes to enrich nucleic acids from the cancerous tissueof the subject for a panel of genomic regions.
 5. The method of claim 1,wherein the first plurality of sequence reads was generated by wholegenome sequencing of nucleic acids from the cancerous tissue of thesubject.
 6. The method of claim 1, wherein the second plurality ofsequence reads was generated by targeted sequencing using a plurality ofnucleic acid probes to enrich nucleic acids from the non-canceroustissue of the subject for a panel of genomic regions.
 7. The method ofclaim 1, wherein the second plurality of sequence reads was generated bywhole genome sequencing of nucleic acids from the non-cancerous tissueof the subject.
 8. The method of claim 1, wherein the measure of theloss of heterozygosity across the genome of the cancerous tissue of thesubject is determined by: determining a loss of genomic heterozygosityin the first plurality of sequence reads, and normalizing the determinedloss of heterozygosity by an estimate of the tumor purity for the firstplurality of sequence reads.
 9. The method of claim 1, wherein theheterozygosity status for the first plurality of DNA damage repair genescomprises a count of the number of unique frameshift mutations detectedin the first plurality of DNA damage repair genes.
 10. The method ofclaim 1, wherein the heterozygosity status for the first plurality ofDNA damage repair genes comprises a count of the number of uniquetruncating mutations detected in the first plurality of DNA damagerepair genes.
 11. The method of claim 1, wherein the first plurality ofDNA damage repair genes comprises BRCA1 and BRCA2.
 12. The method ofclaim 1, wherein the measure of variant alleles detected in the secondplurality of DNA damage repair genes in the genome of the canceroustissue of the subject comprises a count of the number of uniquemutations associated with loss of homologous recombination detected inthe first plurality of sequence reads.
 13. The method of claim 1,wherein the measure of variant alleles detected in the second pluralityof DNA damage repair genes in the genome of the non-cancerous tissue ofthe subject comprises a count of the number of unique mutationsassociated with loss of homologous recombination detected in the secondplurality of sequence reads.
 14. The method of claim 1, wherein thesecond plurality of DNA damage repair genes comprises BRCA1 and BRCA2.15. The method of claim 14, wherein the unique mutations associated withloss of homologous recombination in BRCA1 and BRCA2 include at least 50of the mutations listed in Table
 1. 16. The method of claim 14, whereinthe unique mutations associated with loss of homologous recombination inBRCA1 and BRCA2 comprises the mutations listed in Table
 1. 17. Themethod of claim 1, wherein the method further comprises: when it isdetermined that the cancer in the test subject is homologousrecombination deficient, treating the cancer by administering a poly ADPribose polymerase (PARP) inhibitor to the test subject; and when it isdetermined the cancer in the test subject is not homologousrecombination deficient, treating the cancer with a therapy that doesnot include administration of a PARP inhibitor to the test subject. 18.The method of claim 1, wherein the PARP inhibitor is selected from thegroup consisting of olaparib, veliparib, rucaparib, niraparib, andtalazoparib.
 19. The method of claim 1, wherein the cancer is breastcancer.
 20. The method of claim 1, wherein the cancer is ovarian cancer.21. The method of claim 1, wherein the cancer is colorectal cancer. 22.The method of claim 1, wherein the classifier is a neural networkalgorithm, a support vector machine algorithm, a Naive Bayes algorithm,a nearest neighbor algorithm, a boosted trees algorithm, a random forestalgorithm, a convolutional neural network algorithm, a decision treealgorithm, a regression algorithm, or a clustering algorithm.
 23. Themethod of claim 1, wherein the classifier is a random forest algorithm.24. The method of claim 1, wherein the first plurality of sequence readswas generated by exome sequencing of cDNA molecules generated from thecancerous tissue of the subject.
 25. The method of claim 1, wherein thesecond plurality of sequence reads was generated by exome sequencing ofcDNA molecules generated from the non-cancerous tissue of the subject.26. A computer system comprising: one or more processors; and anon-transitory computer-readable medium including computer-executableinstructions that, when executed by the one or more processors, causethe processors to perform the method of claim
 1. 27. A non-transitorycomputer-readable storage medium having stored thereon program codeinstructions that, when executed by a processor, cause the processor toperform the method of claim
 1. 28. A method for training an algorithmfor determining a homologous recombination pathway status of a cancer,the method comprising: at a computer system comprising at least oneprocessor and a memory storing at least one program for execution by theat least one processor: (A) obtaining, for each respective trainingsubject in a plurality of training subjects with cancer, a correspondinggenomic data construct for the respective training subject, thecorresponding genomic training construct comprising (a) a homologousrecombination pathway status for the cancer of the respective trainingsubject, and (b) one or more features of the genomes of cancerous and anon-cancerous tissues of the respective training subject, the one ormore features including (i) a heterozygosity status for a firstplurality of DNA damage repair genes in the genome of the canceroustissue of the respective training subject, (ii) a measure of the loss ofheterozygosity across the genome of the cancerous tissue of therespective training subject, (iii) a measure of variant alleles detectedin a second plurality of DNA damage repair genes in the genome of thecancerous tissue of the respective training subject, and (iv) a measureof variant alleles detected in the second plurality of DNA damage repairgenes in the genome of the non-cancerous tissue of the respectivetraining subject; and (B) training a classification algorithm against,for each respective training subject, at least (a) the homologousrecombination pathway status for the cancer of the respective trainingsubject, and (b) the plurality of features determined from thecorresponding sample of DNA from the cancerous tissue of the respectivetraining subject.
 29. A computer system comprising: one or moreprocessors; and a non-transitory computer-readable medium includingcomputer-executable instructions that, when executed by the one or moreprocessors, cause the processors to perform the method of claim
 28. 30.A non-transitory computer-readable storage medium having stored thereonprogram code instructions that, when executed by a processor, cause theprocessor to perform the method of claim 28.